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Dissertations / Theses on the topic 'Satellite Image'
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1
Bassett, Robert M. "Automated satellite image navigation." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23552.
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Approved for public release; distribution is unlimitedThis study investigated the automated satellite image navigation method (Auto-Avian) developed and tested by Spaulding (1990) at the Naval Postgraduate School. The Auto-Avian method replaced the manual procedure of selecting Ground Control Points (GCPs) with an autocorrelation process that utilizes the World Vector Shoreline (WVS) provided by the Defense Mapping Agency (DMA) as a "string" of GCPs to rectify satellite images. The automatic cross-correlation of binary references (WVS) and search (image) windows eliminated the subjective error associated with the manual selection of GCPs and produced accuracies comparable to the manual method. This study expanded the scope of Spaulding's (1990) research. The worldwide application of the Auto-Avian method was demonstrated in three world regions (eastern North Pacific Ocean, eastern North Atlantic Ocean, and Persian Gulf). Using five case studies, the performance of the Auto-Avian method on "less than optimum" images (i.e., islands, coastlines affected by lateral distortion and/or cloud cover) was investigated. The result indicated that utilizing the Auto-Avian method on these "less than optimum images" could achieve navigational accuracies approaching those obtained by Spaulding (1990).
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Unsalan, Cem. "Multispectral satellite image understanding." The Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=osu1061903845.
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Spaulding, Brian C. "Automatic satellite image navigation." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA240895.
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Thesis (M.S. in Hydrographic Science)--Naval Postgraduate School, September 1990.Thesis Advisor(s): Wash, C. H. Second Reader: Schnebele, K. J. "September 1990." Description based on title screen as viewed on December 22, 2009. DTIC Descriptor(s): Radiometers, Navigation Reference, Interactions, Accuracy, Theses, Identification, Navigation, Images, Searching, Navigation Satellites, Artificial Satellites, Windows, Vector Analysis, Operators(Personnel), Earth(Planet), Birds, Matching, Automatic Pilots, Shores, Position(Location), Global. DTIC Identifier(s): Satellite Navigation, Program Listings. Author(s) subject terms: Image navigation, binary correlation, automatic landmarking. Includes bibliographical references (p. 78-81). Also available in print.
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Ünsalan, Cem. "Multispectral satellite image understanding." Columbus, Ohio : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5num=osu1061903845.
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Thesis (Ph. D.)--Ohio State University, 2003.Title from first page of PDF file. Document formatted into pages; contains xix, 235 p. : ill. (some col.). Advisor: Kim L. Boyer, Department of Electrical Engineering. Includes bibliographical references (p. 216-235).
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Roman-Gonzalez, Avid. "Compression Based Analysis of Image Artifacts: Application to Satellite Images." Phd thesis, Telecom ParisTech, 2013. http://tel.archives-ouvertes.fr/tel-00935029.
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This thesis aims at an automatic detection of artifacts in optical satellite images such as aliasing, A/D conversion problems, striping, and compression noise; in fact, all blemishes that are unusual in an undistorted image. Artifact detection in Earth observation images becomes increasingly difficult when the resolution of the image improves. For images of low, medium or high resolution, the artifact signatures are sufficiently different from the useful signal, thus allowing their characterization as distortions; however, when the resolution improves, the artifacts have, in terms of signal theory, a similar signature to the interesting objects in an image. Although it is more difficult to detect artifacts in very high resolution images, we need analysis tools that work properly, without impeding the extraction of objects in an image. Furthermore, the detection should be as automatic as possible, given the quantity and ever-increasing volumes of images that make any manual detection illusory. Finally, experience shows that artifacts are not all predictable nor can they be modeled as expected. Thus, any artifact detection shall be as generic as possible, without requiring the modeling of their origin or their impact on an image. Outside the field of Earth observation, similar detection problems have arisen in multimedia image processing. This includes the evaluation of image quality, compression, watermarking, detecting attacks, image tampering, the montage of photographs, steganalysis, etc. In general, the techniques used to address these problems are based on direct or indirect measurement of intrinsic information and mutual information. Therefore, this thesis has the objective to translate these approaches to artifact detection in Earth observation images, based particularly on the theories of Shannon and Kolmogorov, including approaches for measuring rate-distortion and pattern-recognition based compression. The results from these theories are then used to detect too low or too high complexities, or redundant patterns. The test images being used are from the satellite instruments SPOT, MERIS, etc. We propose several methods for artifact detection. The first method is using the Rate-Distortion (RD) function obtained by compressing an image with different compression factors and examines how an artifact can result in a high degree of regularity or irregularity affecting the attainable compression rate. The second method is using the Normalized Compression Distance (NCD) and examines whether artifacts have similar patterns. The third method is using different approaches for RD such as the Kolmogorov Structure Function and the Complexity-to-Error Migration (CEM) for examining how artifacts can be observed in compression-decompression error maps. Finally, we compare our proposed methods with an existing method based on image quality metrics. The results show that the artifact detection depends on the artifact intensity and the type of surface cover contained in the satellite image.
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Tekkaya, Gokhan. "Improving Interactive Classification Of Satellite Image Content." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608326/index.pdf.
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Interactive classifi
cation is an attractive alternative and complementary for automatic classi&
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cation of satellite image content, since the subject is visual and there are not yet powerful computational features corresponding to the sought visual features. In this study, we improve our previous attempt by building a more stable software system with better capabilities for interactive classi&
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cation of the content of satellite images. The system allows user to indicate a few number of image regions that contain a speci&
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c geographical object, for example, a bridge, and to retrieve similar objects on the same satellite images. Retrieval process is iterative in the sense that user guides the classi&
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cation procedure by interaction and visual observation of the results. The classi&
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cation procedure is based on one-class classi&
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Hong, Guowei. "Satellite image processing for remote sensing applications." Thesis, University of Central Lancashire, 1995. http://clok.uclan.ac.uk/1878/.
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This thesis investigates areas of image compression with particular reference to remote sensing imagery. The research described was carried out in four specific areas, namely, discrete cosine transform (DCT) for remote sensing imagery, lossless image compression based on conditional statistics, exploiting interband redundancy for remote sensing imagery, neural networks for lossless image compression. The effect of using standard compression algorithm (JPEG's DCT) on the remote sensing image data is investigated. This involves visual and statistical assessment of the errors produced, both in the data itself, and with reference to the results of the processing (i. e., classification) normally performed using such data. It has been reported that the DCT characteristics can be modified to achieve a trade-off between compression ratio and pixel value error. It is feasible therefore that the user of remote sensing data could find a suitable compromise that could offer some of the compression benefits offered by the DCT, while. retaining sufficient accuracy of image data for the required applications. An approach for lossless image compression using conditional statistics is investigated. That is encoding each pixel value with one of several variable-length codes depending on previous pixel values (context). The author's method achieved its aim by approximating the probability distribution function (PDF) for each context and coding the image data using arithmetic coding. Experimental results are included to show that this method has achieved some improvement in lossless image compression and can achieve an average bits per pixel lower than the zero-order entropy of the prediction-error image. In the area of exploiting interband correlation for remote sensing imagery, two new techniques, namely joint entropy coding and interband prediction, are described. Joint entropy coding is based on the idea that to code a pair of pixel values from two different bands is more effective than to code them individually if there is interband correlation among them. Interband prediction is based on the fact that the structure of one band data can generally give some information about the structure of other bands. The results demonstrate and compare the usefulness of both techniques in improving the overall lossless compression ratio for remote sensing imagery. The idea of using neural networks for lossless image coding is introduced. A novel approach to pixel prediction based on a three-layer perceptron neural network using a backpropagation learning algorithm is described, which is aimed at improving the pixel prediction accuracy, thus improving the lossless compression ratio. Experimental results show this neural network approach consistently achieves better prediction than conventional linear prediction techniques in terms of minimizing the mean square error, although the results for the overall compression ratio are not significantly improved.
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Brewer, Michael Robert. "Neural networks for meteorological satellite image interpretation." Thesis, University of Oxford, 1997. http://ora.ox.ac.uk/objects/uuid:55ee7430-4029-47de-adb7-4b611ba1edc6.
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Meteorological satellite images at visible and infra-red wavelengths are an invaluable source of information on cloud systems because of their extensive coverage of the whole of the Earth's surface, providing data in areas that are only sparsely monitored, if at all, by other means. Although this information has been used subjectively by forecasters for many years, the lack of automatic, quantitative analysis techniques largely prevents its assimilation into numerical weather prediction (NWP) models, which are the basis of all modern weather forecasting. This thesis investigates the use of neural network techniques for the analysis of the images in order to make fuller use of the available data. The recognition of a particular type of cloud is dependent on the determination of a set of features from the satellite image spectral bands that will give discriminating information. This feature extraction and selection process is dealt with in detail, and a feature selection process based on the radial basis function (RBF) neural network is presented. The high-dimensional feature space is visualized on a two-dimensional plane by the use of three techniques: the Kohonen map, the Sammon map, and a recently-developed technique known as the Generative Topographic Mapping (GTM). Classification results using a multi-layer perceptron (MLP) and an RBF neural network are presented. The results of independently classifying each pixel in an image are compared with a method that makes use of contextual information, the Markov Random Field (MRF) model. The limitations of the pixel-based approach are highlighted, and a region-based approach is presented that enables the definition of large-scale regions of uniform cloud type. Two segmentation methods are used, the active contour (or snake) model, and the more recentlydeveloped level set technique. The latter method was found to provide many benefits over the former. The region-based approach will facilitate the assimilation of cloud system information into NWP models in the future.
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Marais, Izak van Zyl. "On-board image quality assessment for a satellite." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/1436.
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Thesis (PhD (Electronic Engineering))--University of Stellenbosch, 2009.The downloading of images is a bottleneck in the image acquisition chain for low earth orbit, remote sensing satellites. An on-board image quality assessment system could optimise use of available downlink time by prioritising images for download, based on their quality. An image quality assessment system based on measuring image degradations is proposed. Algorithms for estimating degradations are investigated. The degradation types considered are cloud cover, additive sensor noise and the defocus extent of the telescope. For cloud detection, the novel application of heteroscedastic discriminant analysis resulted in better performance than comparable dimension reducing transforms from remote sensing literature. A region growing method, which was previously used on-board a micro-satellite for cloud cover estimation, is critically evaluated and compared to commonly used thresholding. The thresholding method is recommended. A remote sensing noise estimation algorithm is compared to a noise estimation algorithm based on image pyramids. The image pyramid algorithm is recommended. It is adapted, which results in smaller errors. A novel angular spectral smoothing method for increasing the robustness of spectral based, direct defocus estimation is introduced. Three existing spectral based defocus estimation methods are compared with the angular smoothing method. An image quality assessment model is developed that models the mapping of the three estimated degradation levels to one quality score. A subjective image quality evaluation experiment is conducted, during which more than 18000 independent human judgements are collected. Two quality assessment models, based on neural networks and splines, are tted to this data. The spline model is recommended. The integrated system is evaluated and image quality predictions are shown to correlate well with human quality perception.
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Vohra, Vijay Kumar. "Map-image registration using automatic extraction of features from high resolution satellite images." Thesis, University College London (University of London), 1999. http://discovery.ucl.ac.uk/1318008/.
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In every part of the world the rate of map revision is alarmingly low, when compared to the rate of change of many human influenced surface features. Map making is very time-consuming and often information used for updates has become history before the updated map is made available. There is therefore a requirement to regularly gather up-to-date information about surface features and to incorporate changes in maps both quickly and efficiently. Automation of two systems, i.e. the automation of map-image registration and then of change detection can fulfill the requirements of map revision. This thesis works on the first system. The piece of work in this study has looked into a fast and an accurate solution to register high resolution satellite images to maps. This will allow changes in ground features to be used to update maps. Photogrammetric techniques used to update maps have previously shown good results, but they are tedious, time-consuming, and not beneficial for updating small changes at all. Feature extraction methods were used in the present study. The system developed was designed for automatic extraction of suitable areal features in images. The emphasis was on areal features rather than point or linear features because they have a distinctive shape, and they are extracted easily from vector as well as raster data. The extraction of suitable polygons, as control information, from images was obtained by using two matching techniques. Patch matching to extract the conjugate map and image polygons, and dynamic programming to find the corresponding matched boundary pixels of the map and image polygons. Some matched points were incorrect because of perspective, shadows and occlusions. A statistical model was developed to remove perspective distortion and large errors. The model demonstrated the removal of erroneous match points, and selected the good match points and registered the images to maps with a sub-pixel accuracy. A novel aspect of the study is that the automation is achieved with high accuracy in flat and moderate terrain areas without using height information, as it is essentially used in photogrammetric techniques.
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Balli, Gulsum Basak. "Micro-satellite Camera Design." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1043769/index.pdf.
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The aim of this thesis has been summarized as the design of a micro-satellite camera
system and its focal plane simulations. The average micro-satellite orbit heights
ranges in between 600-850 km and obviously a multipayload satellite brings volume
and power restrictions for each payload. In this work, an orbit height of 600 km and
a volume of 20×20×
30 cm is assumed, since minimizing the payload dimensions increases the probability of the launch. The pixel size and the dimensions of an imaging detector such as charge-coupled device (CCD) have been defined by the useful image area with acceptable aberration limits on the focal plane. In order to predict the minimum pixel size to be used at the focal plane modulation transfer function (MTF), point spread function (PSF), image distortion and aberration simulations have been carried out and detector parameters for the designed camera have been presented.
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Gu, Degui. "Incorporating structural information into interpretation of satellite images of forests /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/6818.
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Hou, Peixin. "Application based image compression for micro-satellite optical imaging." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/804476/.
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Perry, Michael D. "Value aided satellite altimetry data for weapon presets." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FPerry.pdf.
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Harrell, Andre T. "Wireless technology via satellite communications for peacekeeping operations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://library.nps.navy.mil/uhtbin/hyperion-image/01Sep%5FHarrell.pdf.
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Thesis (M.S. in Information Systems Technology)--Naval Postgraduate School, September 2001.Thesis advisor(s): Tri T. Ha, Nancy Roberts. Includes bibliographical references (p. 85-87). Also available online.
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Sina, Md Ibne. "Satellite Image Processing with Biologically-inspired Computational Methods and Visual Attention." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23122.
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The human vision system is generally recognized as being superior to all known artificial vision systems. Visual attention, among many processes that are related to human vision, is responsible for identifying relevant regions in a scene for further processing. In most cases, analyzing an entire scene is unnecessary and inevitably time consuming. Hence considering visual attention might be advantageous. A subfield of computer vision where this particular functionality is computationally emulated has been shown to retain high potential in solving real world vision problems effectively. In this monograph, elements of visual attention are explored and algorithms are proposed that exploit such elements in order to enhance image understanding capabilities. Satellite images are given special attention due to their practical relevance, inherent complexity in terms of image contents, and their resolution. Processing such large-size images using visual attention can be very helpful since one can first identify relevant regions and deploy further detailed analysis in those regions only. Bottom-up features, which are directly derived from the scene contents, are at the core of visual attention and help identify salient image regions. In the literature, the use of intensity, orientation and color as dominant features to compute bottom-up attention is ubiquitous. The effects of incorporating an entropy feature on top of the above mentioned ones are also studied. This investigation demonstrates that such integration makes visual attention more sensitive to fine details and hence retains the potential to be exploited in a suitable context. One interesting application of bottom-up attention, which is also examined in this work, is that of image segmentation. Since low salient regions generally correspond to homogenously textured regions in the input image; a model can therefore be learned from a homogenous region and used to group similar textures existing in other image regions. Experimentation demonstrates that the proposed method produces realistic segmentation on satellite images. Top-down attention, on the other hand, is influenced by the observer’s current states such as knowledge, goal, and expectation. It can be exploited to locate target objects depending on various features, and increases search or recognition efficiency by concentrating on the relevant image regions only. This technique is very helpful in processing large images such as satellite images. A novel algorithm for computing top-down attention is proposed which is able to learn and quantify important bottom-up features from a set of training images and enhances such features in a test image in order to localize objects having similar features. An object recognition technique is then deployed that extracts potential target objects from the computed top-down attention map and attempts to recognize them. An object descriptor is formed based on physical appearance and uses both texture and shape information. This combination is shown to be especially useful in the object recognition phase. The proposed texture descriptor is based on Legendre moments computed on local binary patterns, while shape is described using Hu moment invariants. Several tools and techniques such as different types of moments of functions, and combinations of different measures have been applied for the purpose of experimentations. The developed algorithms are generalized, efficient and effective, and have the potential to be deployed for real world problems. A dedicated software testing platform has been designed to facilitate the manipulation of satellite images and support a modular and flexible implementation of computational methods, including various components of visual attention models.
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Lau, King Shing Albert. "Application of image analysis techniques to satellite cloud motion tracking." Thesis, University of Plymouth, 1992. http://hdl.handle.net/10026.1/1131.
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Cloud motion wind (CMW) determination requires tracking of individual cloud targets. This is achieved by first clustering and then tracking each cloud cluster. Ideally, different cloud clusters correspond to diiferent pressure levels. Two new clustering techniques have been developed for the identification of cloud types in multi-spectral satellite imagery. The first technique is the Global-Local clustering algorithm. It is a cascade of a histogram clustering algorithm and a dynamic clustering algorithm. The histogram clustering algorithm divides the multi-spectral histogram into'non-overlapped regions, and these regions are used to initialise the dynamic clustering algorithm. The dynamic clustering algorithm assumes clusters have a Gaussian distributed probability density function with diiferent population size and variance. The second technique uses graph theory to exploit the spatial information which is often ignored in per-pixel clustering. The algorithm is in two stages: spatial clustering and spectral clustering. The first stage extracts homogeneous objects in the image using a family of algorithms based on stepwise optimization. This family of algorithms can be further divided into two approaches: Top-down and Bottom-up. The second stage groups similar segments into clusters using a statistical hypothesis test on their similarities. The clusters generated are less noisy along class boundaries and are in hierarchical order. A criterion based on mutual information is derived to monitor the spatial clustering process and to suggest an optimal number of segments. An automated cloud motion tracking program has been developed. Three images (each separated by 30 minutes) are used to track cloud motion and the middle image is clustered using Global-Local clustering prior to tracking. Compared with traditional methods based on raw images, it is found that separation of cloud types before cloud tracking can reduce the ambiguity due to multi-layers of cloud moving at different speeds and direction. Three matching techniques are used and their reliability compared. Target sizes ranging from 4 x 4 to 32 x 32 are tested and their errors compared. The optimum target size for first generation METEOSAT images has also been found.
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GHASSEMI, SINA. "Deep Learning for Image Analysis in Satellite and Traffic Applications." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2740594.
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Kalinicheva, Ekaterina. "Unsupervised satellite image time series analysis using deep learning techniques." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS335.
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Cette thèse présente un ensemble d'algorithmes non-supervisés pour l'analyse générique de séries temporelles d'images satellites (STIS). Nos algorithmes exploitent des méthodes de machine learning et, notamment, les réseaux de neurones afin de détecter les différentes entités spatio-temporelles et leurs changements éventuels dans le temps. Nous visons à identifier trois types de comportement temporel : les zones sans changements, les changements saisonniers, les changements non triviaux (changements permanents comme les constructions, la rotation des cultures agricoles, etc).Par conséquent, nous proposons deux frameworks : pour la détection et le clustering des changements non-triviaux et pour le clustering des changements saisonniers et des zones sans changements. Le premier framework est composé de deux étapes : la détection de changements bi-temporels et leur interprétation dans le contexte multi-temporel avec une approche basée graphes. La détection de changements bi-temporels est faite pour chaque couple d’images consécutives et basée sur la transformation des features avec les autoencodeurs (AEs). A l’étape suivante, les changements à différentes dates qui appartiennent à la même zone géographique forment les graphes d’évolution qui sont par la suite clusterisés avec un modèle AE de réseaux de neurones récurrents. Le deuxième framework présente le clustering basé objets de STIS. Premièrement, la STIS est encodée en image unique avec un AE convolutif 3D multi-vue. Dans un deuxième temps, nous faisons la segmentation en deux étapes en utilisant à la fois l’image encodée et la STIS. Finalement, les segments obtenus sont clusterisés avec leurs descripteurs encodésThis thesis presents a set of unsupervised algorithms for satellite image time series (SITS) analysis. Our methods exploit machine learning algorithms and, in particular, neural networks to detect different spatio-temporal entities and their eventual changes in the time.In our thesis, we aim to identify three different types of temporal behavior: no change areas, seasonal changes (vegetation and other phenomena that have seasonal recurrence) and non-trivial changes (permanent changes such as constructions or demolishment, crop rotation, etc). Therefore, we propose two frameworks: one for detection and clustering of non-trivial changes and another for clustering of “stable” areas (seasonal changes and no change areas). The first framework is composed of two steps which are bi-temporal change detection and the interpretation of detected changes in a multi-temporal context with graph-based approaches. The bi-temporal change detection is performed for each pair of consecutive images of the SITS and is based on feature translation with autoencoders (AEs). At the next step, the changes from different timestamps that belong to the same geographic area form evolution change graphs. The graphs are then clustered using a recurrent neural networks AE model to identify different types of change behavior. For the second framework, we propose an approach for object-based SITS clustering. First, we encode SITS with a multi-view 3D convolutional AE in a single image. Second, we perform a two steps SITS segmentation using the encoded SITS and original images. Finally, the obtained segments are clustered exploiting their encoded descriptors
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Hittner, Andrew J. "Detecting and measuring temporal phenomenon with high resolution satellite imagery." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FHittner.pdf.
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Thesis (M.S. in Space Systems Operations)--Naval Postgraduate School, September 2003.Thesis advisor(s): Richard Olsen, Richard Harkins. Includes bibliographical references (p. 43). Also available online.
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Poulsen, Andrew Joseph. "Real-time Adaptive Cancellation of Satellite Interference in Radio Astronomy." Diss., CLICK HERE for online access, 2003. http://contentdm.lib.byu.edu/ETD/image/etd238.pdf.
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Vincent, Dominick A. "Visibility over land from contrast analysis of multi-spectral satellite /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FVincent.pdf.
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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2003.Thesis advisor(s): Philip A. Durkee, Carlyle H. Wash. Includes bibliographical references (p. 49-51). Also available online.
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Nolte, Ernst Hendrik. "Image compression quality measurement : a comparison of the performance of JPEG and fractal compression on satellite images." Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51796.
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Thesis (MEng)--Stellenbosch University, 2000.ENGLISH ABSTRACT: The purpose of this thesis is to investigate the nature of digital image compression and the calculation of the quality of the compressed images. The work is focused on greyscale images in the domain of satellite images and aerial photographs. Two compression techniques are studied in detail namely the JPEG and fractal compression methods. Implementations of both these techniques are then applied to a set of test images. The rest of this thesis is dedicated to investigating the measurement of the loss of quality that was introduced by the compression. A general method for quality measurement (signal To Noise Ratio) is discussed as well as a technique that was presented in literature quite recently (Grey Block Distance). Hereafter, a new measure is presented. After this, a means of comparing the performance of these measures is presented. It was found that the new measure for image quality estimation performed marginally better than the SNR algorithm. Lastly, some possible improvements on this technique are mentioned and the validity of the method used for comparing the quality measures is discussed.
AFRIKAANSE OPSOMMING: Die doel van hierdie tesis is om ondersoek in te stel na die aard van digitale beeldsamepersing en die berekening van beeldkwaliteit na samepersing. Daar word gekonsentreer op grysvlak beelde in die spesifieke domein van satellietbeelde en lugfotos. Twee spesifieke samepersingstegnieke word in diepte ondersoek naamlik die JPEG en fraktale samepersingsmetodes. Implementasies van beide hierdie tegnieke word op 'n stel toetsbeelde aangewend. Die res van hierdie tesis word dan gewy aan die ondersoek van die meting van die kwaliteitsverlies van hierdie saamgeperste beelde. Daar word gekyk na 'n metode wat in algemene gebruik in die praktyk is asook na 'n nuwer metode wat onlangs in die literatuur veskyn het. Hierna word 'n nuwe tegniek bekendgestel. Verder word daar 'n vergelyking van hierdie mates en 'n ondersoek na die interpretasie van die 'kwaliteit' van hierdie kwaliteitsmate gedoen. Daar is gevind dat die nuwe maatstaf vir kwaliteit net so goed en selfs beter werk as die algemene maat vir beeldkwaliteit naamlik die Sein tot Ruis Verhouding. Laastens word daar moontlike verbeterings op die maatstaf genoem en daar volg 'n bespreking oor die geldigheid van die metode wat gevolg is om die kwaliteit van die kwaliteitsmate te bepaal
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Flowerdew, Roland John. "Atmospheric correction for the visible and near-infrared channels of ATSR-2." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283392.
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Yoho, Peter K. "Satellite scatterometers : calibration using a ground station and statistical measurement theory /." Diss., CLICK HERE for online access, 2003. http://contentdm.lib.byu.edu/ETD/image/etd306.pdf.
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Dalay, Oral. "Interactive Classification Of Satellite Image Content Based On Query By Example." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/2/12607039/index.pdf.
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In our attempt to construct a semantic filter for satellite image content, we have built a software that allows user to indicate a few number of image regions that contains a specific geographical object, such as, a bridge, and to retrieve similar objects on the same satellite image. We are particularly interested in performing a data analysis approach based on user interaction. User can guide the classification procedure by interaction and visual observation of the results. We have applied a two step procedure for this and preliminary results show that we eliminate many true negatives while keeping most of the true positives.
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Rosander, Christian. "Characteristics of convective cloud cluster formationover Thailand through satellite image analysis." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303916.
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Weather forecasting relies on the availability of observational data as input parameters. However,such data are not readily available, because of difficulties to collect weather data due toinaccessibility to many places in the world, such as oceans or mountain regions. For this reason,satellite surveillance is a suitable tool to observe the atmosphere in regions where it is notpossible by other means. This master thesis is a study of convective cloud cluster formation over Thailand, conductedthrough satellite image analysis. Characteristics of cloud cluster formations are investigatedthrough an implementation of the Maximum Spatial Correlation Technique (MASCOTTE),described by Carvalho and Jones (2001). This method allows tracking of convective cloud systemsthrough region based analysis of satellite images. The aim of this study is to investigate whether satellite image analysis, through the implementationof the MASCOTTE methodology, can provide characteristics of convective cloud systems,in order to discern convective systems by intensity, accurately enough to be able to discernsevere thunderstorms from ordinary thunderstorms. The annual distribution of the occurrenceof life cycles detected through the analysis is studied, as well as their monthly distribution ofmean and maximum life times. Moreover, the yearly distribution of life cycle mean and minimumbrightness temperatures are analysed, as well as the number of detected split and mergeevents. This is followed by a comparison of life cycle structural properties to investigate thepossibility to use individual parameters, alone or in combination with each other, as indicatorsof the degree of convective activity within life cycles. Yearly distributions were studied in order to verify if this method could reveal seasonal variations,such as the onset period of the wet season, in terms of the occurrence of life cycles andtheir life time. The findings of this study verified that the most convectively intense life cycles exist under theinfluence of the Inter Tropical Convergence Zone (ITCZ), during the onset and beginning ofthe monsoon season. Analysis of life cycle structural properties, showed that properties likemean and minimum brightness temperature as well as fractional convective area, could be usedas indicators to discern between life cycles with different level of convective activity. However,it is concluded that studies, including ground-based remote sensing technologies such asRADAR/LIDAR, as well as data from rawinsondes, needs to be conducted in order to clarifyif it is possible to use this methodology to successfully discern severe thunderstorms fromordinary thunderstorms.Tillgängligheten av meteorologiska mätdata är väsentlig för att kunna prognostisera väder. Idag är tillgängligheten på dessa data relativt gles, bland annat på grund av svårigheter att mäta på många platser runt om i världen, t.ex över världshaven eller vid otillgängliga bergsområden. Därför är satellitövervakning ett bra alternativ till andra typer av väderobservationer, eftersom denna teknik kan tillhandahålla mätdata över stora områden som annars inte är möljiga att samla data från. Denna magisteruppsats är en studie om egenskaper hos konvektiv molnbildning över Thailand. Studien är genomförd med hjälp av satellitbildsanalys. Egenskaper hos olika konvektiva molnceller har studerats genom att använda en metod baserad på ”the Maximum Spatial Correlation Technique” (MASCOTTE), beskriven av Carvalho and Jones (2001). Tanken bakom denna metod är att hitta och följa utvecklingen av olika konvektiva molnceller baserat på deras storlek och temperatur. Målet med studien är att undersöka hurvida denna metoden kan ge kunskap som leder till att man kan skilja på konvektiva celler, genom intensitetsskillnader, med tillräcklig noggrannhet för att kunna urskilja vanliga konvektiva celler från intensiva celler. För att få en uppfattning om förekomsten av intensiva konvektiva system, har antalet detekterade livscykler per månad studerats. För sedan att få en bild av hurvida deras livscykler skiljer sig åt över året, har även egenskaper som medellivslängd och maximal livslängd studerats. Dessutom studerades den årliga fördelningen av livscyklernas medel och minimum temperaturer, samt förekomsten av delningar och sammanslagningar av konvektiva celler. För att finna kunskap om skillnader i intensitet mellan individuella livscykler, har egenskaper som medel och minimum temperatur analyserats. Dessutom har andelen moln med extremt låg temperatur studerats i syfte att kunna använda dessa parametrar som intensitetsindikatorer vid satellitbildsanalys. Resultaten i denna studie visar att de mest intensiva konvektiva molnsystemen (kraftigaste åskvädren), förekommer under påverkan av ITCZ (Inter Tropical Convergence Zone), under antågandet och början av regnperioden. Studier av de konvektiva systemens egenskaper visade att parametrar, som andelen extremt kallt område i molnceller (fractional convective area), och livscyklernas medel och minimum temperaturer, skulle kunna användas som intensitetsindikatorer för att skilja på olika livscykler med avseende på deras styrka i intensitet. Slutsatsen av studien är att det behövs fler studier där andra typer av meteorologiska mätdata, såsom RADAR/LIDAR och sonderingsdata är involverade, för att skaffa ytterligare kunskap om hur man genom satellitbildsanalys kan urskilja kraftiga åskväder.
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Nekkanti, Veera Venkata Satyanarayana, and Kaushik Sai Srinivas Nalajala. "Super Resolution Image Reconstruction for Indian Remote Sensing Satellite (Cartosat-1)." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14438.
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Rais, Martin. "Fast and accurate image registration. Applications to on-board satellite imaging." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLN077/document.
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Cette thèse commence par une étude approfondie des méthodes d’estimation de décalage sous-pixeliques rapides. Une comparaison complète est effectuée prenant en compte problèmes d’estimation de décalage existant dans des applications réelles, à savoir, avec différentes conditions de SNR, différentes grandeurs de déplacement, la non préservation de la contrainte de luminosité constante, l’aliasing et, surtout, la limitation des ressources de calcul. Sur la base de cette étude, en collaboration avec le CNES (l’agence spatiale française), deux problèmes qui sont cruciaux pour l’optique numérique des satellites d’observation de la terre sont analysés. Nous étudions d’abord le problème de correction de front d’onde dans le contexte de l’optique actif. Nous proposons un algorithme pour mesurer les aberrations de front d’onde sur un senseur de type Shack-Hartmann (SHWFS en anglais) en observant la terre. Nous proposons ici une revue de l’état de l’art des méthodes pour le SHWFS utilisé sur des scènes étendues (comme la terre) et concevons une nouvelle méthode pour améliorer l’estimation de front d’onde, en utilisant une approche basée sur l’équation du flot optique. Nous proposons également deux méthodes de validation afin d’assurer une estimation correcte du front d’onde sur les scènes étendues. Tandis que la première est basée sur une adaptation numérique des bornes inférieures (théoriques) pour le recalage d’images, la seconde méthode défausse rapidement les paysages en se basant sur la distribution des gradients. La deuxième application de satellite abordée est la conception numérique d’une nouvelle génération de senseur du type Time Delay Integration (TDI). Dans ce nouveau concept, la stabilisation active en temps réel du TDI est réalisée pour étendre considérablement le temps d’intégration, et donc augmenter le RSB des images. Les lignes du TDI ne peuvent pas être fusionnées directement par addition parce que leur position est modifiée par des microvibrations. Celles-ci doivent être compensées en temps réel avec une précision sous-pixellique. Nous étudions les limites fondamentales théoriques de ce problème et proposons une solution qui s’en approche. Nous présentons un système utilisant la convolution temporelle conjointement à une estimation en ligne du bruit de capteur, à une estimation de décalage basée sur les gradients et à une méthode multiimage non conventionnelle pour mesurer les déplacements globaux. Les résultats obtenus sont concluants sur les fronts de la précision et de la complexité. Pour des modèles de transformation plus complexes, une nouvelle méthode effectuant l’estimation précise et robuste des modèles de mise en correspondance des points d’intérêt entre images est proposée. La difficulté provenant de la présence de fausses correspondances et de mesures bruitées conduit à un échec des méthodes de régression traditionnelles. En vision par ordinateur, RANSAC est certainement la méthode la plus utilisée pour surmonter ces difficultés. RANSAC est capable de discriminer les fausses correspondances en générant de façon aléatoire des hypothèses et en vérifiant leur consensus. Cependant, sa réponse est basée sur la seule itération qui a obtenu le consensus le plus large, et elle ignore toutes les autres hypothèses. Nous montrons ici que la précision peut être améliorée en agrégeant toutes les hypothèses envisagées. Nous proposons également une stratégie simple qui permet de moyenner rapidement des transformations 2D, ce qui réduit le coût supplémentaire de calcul à quantité négligeable. Nous donnons des applications réelles pour estimer les transformations projectives et les transformations homographie + distorsion. En incluant une adaptation simple de LO-RANSAC dans notre cadre, l’approche proposée bat toutes les méthodes de l’état de l’art. Une analyse complète de l’approche proposée est réalisée, et elle démontre un net progrès en précision, stabilité et polyvalenceThis thesis starts with an in-depth study of fast and accurate sub-pixel shift estimationmethods. A full comparison is performed based on the common shift estimation problems occurring in real-life applications, namely, varying SNR conditions, differentdisplacement magnitudes, non-preservation of the brightness constancy constraint, aliasing, and most importantly, limited computational resources. Based on this study, in collaboration with CNES (the French space agency), two problems that are crucial for the digital optics of earth-observation satellites are analyzed.We first study the wavefront correction problem in an active optics context. We propose a fast and accurate algorithm to measure the wavefront aberrations on a Shack-HartmannWavefront Sensor (SHWFS) device observing the earth. We give here a review of state-of-the-art methods for SHWFS used on extended scenes (such as the earth) and devise a new method for improving wavefront estimation, based on a carefully refined approach based on the optical flow equation. This method takes advantage of the small shifts observed in a closed-loop wavefront correction system, yielding improved accuracy using fewer computational resources. We also propose two validation methods to ensure a correct wavefront estimation on extended scenes. While the first one is based on a numerical adaptation of the (theoretical) lower bounds of image registration, the second method rapidly discards landscapes based on the gradient distribution, inferred from the Eigenvalues of the structure tensor.The second satellite-based application that we address is the numerical design of a new generation of Time Delay Integration (TDI) sensor. In this new concept, active real-time stabilization of the TDI is performed to extend considerably the integration time, and therefore to boost the images SNR. The stripes of the TDI cannot be fused directly by addition because their position is altered by microvibrations. These must be compensated in real time using limited onboard computational resources with high subpixel accuracy. We study the fundamental performance limits for this problem and propose a real-time solution that nonetheless gets close to the theoretical limits. We introduce a scheme using temporal convolution together with online noise estimation, gradient-based shift estimation and a non-conventional multiframe method for measuring global displacements. The obtained results are conclusive on the fronts of accuracy and complexity and have strongly influenced the final decisions on the future configurations of Earth observation satellites at CNES.For more complex transformation models, a new image registration method performing accurate robust model estimation through point matches between images is proposed here. The difficulty coming from the presence of outliers causes the failure of traditional regression methods. In computer vision, RANSAC is definitely the most renowned method that overcomes such difficulties. It discriminates outliers by randomly generating minimalist sampled hypotheses and verifying their consensus over the input data. However, its response is based on the single iteration that achieved the largest inlier support, while discarding all other generated hypotheses. We show here that the resulting accuracy can be improved by aggregating all hypotheses. We also propose a simple strategy that allows to rapidly average 2D transformations, leading to an almost negligible extra computational cost. We give practical applications to the estimation of projective transforms and homography+distortion transforms. By including a straightforward adaptation of the locally optimized RANSAC in our framework, the proposed approach improves over every other available state-of-the-art method. A complete analysis of the proposed approach is performed, demonstrating its improved accuracy, stability and versatility
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Sanchez, Eduardo Hugo. "Learning disentangled representations of satellite image time series in a weakly supervised manner." Thesis, Toulouse 3, 2021. http://www.theses.fr/2021TOU30032.
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Cette thèse se focalise sur l'apprentissage de représentations de séries temporelles d'images satellites via des méthodes d'apprentissage non supervisé. Le but principal est de créer une représentation qui capture l'information la plus pertinente de la série temporelle afin d'effectuer d'autres applications d'imagerie satellite. Cependant, l'extraction d'information à partir de la donnée satellite implique de nombreux défis. D'un côté, les modèles doivent traiter d'énormes volumes d'images fournis par les satellites. D'un autre côté, il est impossible pour les opérateurs humains d'étiqueter manuellement un tel volume d'images pour chaque tâche (par exemple, la classification, la segmentation, la détection de changement, etc.). Par conséquent, les méthodes d'apprentissage supervisé qui ont besoin des étiquettes ne peuvent pas être appliquées pour analyser la donnée satellite. Pour résoudre ce problème, des algorithmes d'apprentissage non supervisé ont été proposés pour apprendre la structure de la donnée au lieu d'apprendre une tâche particulière. L'apprentissage non supervisé est une approche puissante, car aucune étiquette n'est nécessaire et la connaissance acquise sur la donnée peut être transférée vers d'autres tâches permettant un apprentissage plus rapide avec moins d'étiquettes. Dans ce travail, on étudie le problème de l'apprentissage de représentations démêlées de séries temporelles d'images satellites. Le but consiste à créer une représentation partagée qui capture l'information spatiale de la série temporelle et une représentation exclusive qui capture l'information temporelle spécifique à chaque image. On présente les avantages de créer des représentations spatio-temporelles. Par exemple, l'information spatiale est utile pour effectuer la classification ou la segmentation d'images de manière invariante dans le temps tandis que l'information temporelle est utile pour la détection de changement. Pour ce faire, on analyse plusieurs modèles d'apprentissage non supervisé tels que l'auto-encodeur variationnel (VAE) et les réseaux antagonistes génératifs (GANs) ainsi que les extensions de ces modèles pour effectuer le démêlage des représentations. Considérant les résultats impressionnants qui ont été obtenus par les modèles génératifs et reconstructifs, on propose un nouveau modèle qui crée une représentation spatiale et une représentation temporelle de la donnée satellite. On montre que les représentations démêlées peuvent être utilisées pour effectuer plusieurs tâches de vision par ordinateur surpassant d'autres modèles de l'état de l'art. Cependant, nos expériences suggèrent que les modèles génératifs et reconstructifs présentent des inconvénients liés à la dimensionnalité de la représentation, à la complexité de l'architecture et au manque de garanties sur le démêlage. Pour surmonter ces limitations, on étudie une méthode récente basée sur l'estimation et la maximisation de l'informations mutuelle sans compter sur la reconstruction ou la génération d'image. On propose un nouveau modèle qui étend le principe de maximisation de l'information mutuelle pour démêler le domaine de représentation. En plus des expériences réalisées sur la donnée satellite, on montre que notre modèle est capable de traiter différents types de données en étant plus performant que les méthodes basées sur les GANs et les VAEs. De plus, on prouve que notre modèle demande moins de puissance de calcul et pourtant est plus efficace. Enfin, on montre que notre modèle est utile pour créer une représentation qui capture uniquement l'information de classe entre deux images appartenant à la même catégorie. Démêler la classe ou la catégorie d'une image des autres facteurs de variation permet de calculer la similarité entre pixels et effectuer la segmentation d'image d'une manière faiblement superviséeThis work focuses on learning data representations of satellite image time series via an unsupervised learning approach. The main goal is to enforce the data representation to capture the relevant information from the time series to perform other applications of satellite imagery. However, extracting information from satellite data involves many challenges since models need to deal with massive amounts of images provided by Earth observation satellites. Additionally, it is impossible for human operators to label such amount of images manually for each individual task (e.g. classification, segmentation, change detection, etc.). Therefore, we cannot use the supervised learning framework which achieves state-of-the-art results in many tasks.To address this problem, unsupervised learning algorithms have been proposed to learn the data structure instead of performing a specific task. Unsupervised learning is a powerful approach since no labels are required during training and the knowledge acquired can be transferred to other tasks enabling faster learning with few labels.In this work, we investigate the problem of learning disentangled representations of satellite image time series where a shared representation captures the spatial information across the images of the time series and an exclusive representation captures the temporal information which is specific to each image. We present the benefits of disentangling the spatio-temporal information of time series, e.g. the spatial information is useful to perform time-invariant image classification or segmentation while the knowledge about the temporal information is useful for change detection. To accomplish this, we analyze some of the most prevalent unsupervised learning models such as the variational autoencoder (VAE) and the generative adversarial networks (GANs) as well as the extensions of these models to perform representation disentanglement. Encouraged by the successful results achieved by generative and reconstructive models, we propose a novel framework to learn spatio-temporal representations of satellite data. We prove that the learned disentangled representations can be used to perform several computer vision tasks such as classification, segmentation, information retrieval and change detection outperforming other state-of-the-art models. Nevertheless, our experiments suggest that generative and reconstructive models present some drawbacks related to the dimensionality of the data representation, architecture complexity and the lack of disentanglement guarantees. In order to overcome these limitations, we explore a recent method based on mutual information estimation and maximization for representation learning without relying on image reconstruction or image generation. We propose a new model that extends the mutual information maximization principle to disentangle the representation domain into two parts. In addition to the experiments performed on satellite data, we show that our model is able to deal with different kinds of datasets outperforming the state-of-the-art methods based on GANs and VAEs. Furthermore, we show that our mutual information based model is less computationally demanding yet more effective. Finally, we show that our model is useful to create a data representation that only captures the class information between two images belonging to the same category. Disentangling the class or category of an image from other factors of variation provides a powerful tool to compute the similarity between pixels and perform image segmentation in a weakly-supervised manner
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Auli-Llinas, Francesc, Michael W. Marcellin, Victor Sanchez, Joan Serra-Sagrista, Joan Bartrina-Rapesta, and Ian Blanes. "Coding Scheme for the Transmission of Satellite Imagery." IEEE, 2016. http://hdl.handle.net/10150/623188.
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The coding and transmission of the massive datasets captured by Earth Observation (EO) satellites is a critical issue in current missions. The conventional approach is to use compression on board the satellite to reduce the size of the captured images. This strategy exploits spatial and/or spectral redundancy to achieve compression. Another type of redundancy found in such data is the temporal redundancy between images of the same area that are captured at different instants of time. This type of redundancy is commonly not exploited because the required data and computing power are not available on board the satellite. This paper introduces a coding scheme for EO satellites able to exploit this redundancy. Contrary to traditional approaches, the proposed scheme employs both the downlink and the uplink of the satellite. Its main insight is to compute and code the temporal redundancy on the ground and transmit it to the satellite via the uplink. The satellite then uses this information to compress more efficiently the captured image. Experimental results for Landsat 8 images indicate that the proposed dual link image coding scheme can achieve higher coding performance than traditional systems for both lossless and lossy regimes.
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Blazye, Christopher J. "An assessment of satellite remote sensing for land cover classification." Thesis, University of Nottingham, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277180.
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Paithoonwattanakij, Kitti. "Automatic pattern recognition techniques for geometrical correction on satellite data." Thesis, University of Dundee, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293190.
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Fröjse, Linda. "Multitemporal Satellite Images for Urban Change Detection." Thesis, KTH, Geoinformatik och Geodesi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-38539.
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The objective of this research is to detect change in urban areas using two satellite images (from 2001 and 2010) covering the city of Shanghai, China. These satellite images were acquired by Landsat-7 and HJ-1B, two satellites with different sensors. Two change detection algorithms were tested: image differencing and post-classification comparison. For image differencing the difference image was classified using unsupervised k-means classification, the classes were then aggregated into change and no change by visual inspection. For post-classification comparison the images were classified using supervised maximum likelihood classification and then the difference image of the two classifications were classified into change and no change also by visual inspection. Image differencing produced result with poor overall accuracy (band 2: 24.07%, band 3: 25.96%, band 4: 46.93%), while post-classification comparison produced result with better overall accuracy (90.96%). Post-classification comparison works well with images from different sensors, but it relies heavily on the accuracy of the classification. The major downside of the methodology of both algorithms was the large amount of visual inspection.
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Zraqou, Jamal Sami. "Automated system design for the efficient processing of solar satellite images : developing novel techniques and software platform for the robust feature detection and the creation of 3D anaglyphs and super-resolution images for solar satellite images." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5434.
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The Sun is of fundamental importance to life on earth and is studied by scientists from many disciplines. It exhibits phenomena on a wide range of observable scales, timescales and wavelengths and due to technological developments there is a continuing increase in the rate at which solar data is becoming available for study which presents both opportunities and challenges. Two satellites recently launched to observe the sun are STEREO (Solar TErrestrial RElations Observatory), providing simultaneous views of the SUN from two different viewpoints and SDO (Solar Dynamics Observatory) which aims to study the solar atmosphere on small scales and times and in many wavelengths. The STEREO and SDO missions are providing huge volumes of data at rates of about 15 GB per day (initially it was 30 GB per day) and 1.5 terabytes per day respectively. Accessing these huge data volumes efficiently at both high spatial and high time resolutions is important to support scientific discovery but requires increasingly efficient tools to browse, locate and process specific data sets. This thesis investigates the development of new technologies for processing information contained in multiple and overlapping images of the same scene to produce images of improved quality. This area in general is titled Super Resolution (SR), and offers a technique for reducing artefacts and increasing the spatial resolution. Another challenge is to generate 3D images such as Anaglyphs from uncalibrated pairs of SR images. An automated method to generate SR images is presented here. The SR technique consists of three stages: image registration, interpolation and filtration. Then a method to produce enhanced, near real-time, 3D solar images from uncalibrated pairs of images is introduced. Image registration is an essential enabling step in SR and Anaglyph processing. An accurate point-to-point mapping between views is estimated, with multiple images registered using only information contained within the images themselves. The performances of the proposed methods are evaluated using benchmark evaluation techniques. A software application called the SOLARSTUDIO has been developed to integrate and run all the methods introduced in this thesis. SOLARSTUDIO offers a number of useful image processing tools associated with activities highly focused on solar images including: Active Region (AR) segmentation, anaglyph creation, solar limb extraction, solar events tracking and video creation.
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Lucier, Jordan W. "Automatic UAV image registration using feature detection and matching with satellite imagery." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119920.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 45-46).
This thesis presents an approach and implementation for using satellite imagery to perform image registration with unprocessed aerial images captured with a UAV. Aerial imagery is used in a large variety of applications including disaster relief, urban planning, crop and vegetation monitoring, and mapping. The common difficulty in utilizing aerial imagery captured by aircraft rather than satellite is that of image registration: transforming data into the image coordinate system. Often, the goal of these applications involves transforming location data into the image coordinate system for object extraction and further processing. Current approaches require hand-labeling of correspondences, the use of ground control points (GCPs), or human analysis to identify objects or locations of interest in the aerial imagery. As such, these methods do not sufficiently scale, generalize, or provide the efficiency required for these applications. The proposed approach to image registration in aerial imagery makes use of raw images captured on consumer-grade UAVs, and uses automatic feature detection and matching to register the imagery. This implementation provides a proof of concept, which was found to succeed on roughly 65% of our images.
by Jordan W. Lucier.
M. Eng.
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Wang, Zhihao. "Land Cover Classification on Satellite Image Time Series Using Deep Learning Models." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu159559249009195.
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Museler, Erica A. "A comparison of in-situ measurements and satellite remote sensing of underwater visibility." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FMuseler.pdf.
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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, March 2003.Thesis advisor(s): Philip A. Durkee. Includes bibliographical references (p. 57-59). Also available online.
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Sefara, Mamphoko Nelly. "Design of a forward error correction algorithm for a satellite modem." Thesis, Stellenbosch : Stellenbosch University, 2001. http://hdl.handle.net/10019.1/52181.
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Thesis (MScEng)--University of Stellenbosch, 2001.ENGLISH ABSTRACT: One of the problems with any deep space communication system is that information may be altered or lost during transmission due to channel noise. It is known that any damage to the bit stream may lead to objectionable visual quality distortion of images at the decoder. The purpose of this thesis is to design an error correction and data compression algorithm for image protection, which will allow the communication bandwidth to be better utilized. The work focuses on Sunsat (Stellenbosch Satellite) images as test images. Investigations were done on the JPEG 2000 compression algorithm's robustness to random errors, putting more emphasis on how much of the image is degraded after compression. Implementation of both the error control coding and data compression strategy is then applied to a set of test images. The FEe algorithm combats some if not all of the simulated random errors introduced by the channel. The results illustrates that the error correction of random errors is achieved by a factor of 100 times (xl00) on all test images and that the probability of error of 10-2in the channel (10-4for image data) shows that the errors causes little degradation on the image quality.
AFRIKAANSE OPSOMMING: Een van die probleme met kommunikasie in die ruimte is dat informasie mag verlore gaan en! of gekorrupteer word deur ruis gedurende versending deur die kanaal. Dit is bekend dat enige skade aan die bisstroom mag lei tot hinderlike vervorming van die beelde wat op aarde ontvang word. Die doel van hierdie tesis om foutkorreksie en datakompressie te ontwikkel wat die satelliet beelde sal beskerm gedurende versending en die kommunikasie kanaal se bandwydte beter sal benut. Die werk fokus op SUNSAT (Stellenbosch Universiteit Satelliet) se beelde as toetsbeelde. Ondersoeke is gedoen na die JPEG2000 kompressie algoritme se bestandheid teen toevalsfoute, met klem op hoeveel die beeld gedegradeer word deur die bisfoute wat voorkom. Beide die kompressie en die foutkorreksie is ge-implementeer en aangewend op die toetsbeelde. Die foutkorreksie bestry die gesimuleerde toevalsfoute, soos wat dit op die kanaal voorkom. Die resultate toon dat die foutkorreksie die toevalsfoute met 'n faktor 100 verminder, en dat 'n foutwaarskynlikheid van 10-2 op die kanaal (10-4 op die beelddata) weinig degradering in die beeldkwaliteit veroorsaak.
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Jagannathan, S. "Coding of satellite image data." Thesis, 1998. http://hdl.handle.net/2009/2715.
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Uma, Guguloth. "Intensity based image registration of satellite images using evolutionary techniques." Thesis, 2014. http://ethesis.nitrkl.ac.in/6412/1/E-90.pdf.
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Image registration is the fundamental image processing technique to determine geometrical transformation that gives the most accurate match between reference and floating images. Its main aim is to align two images. Satellite images to be fused for numerous applications must be registered before use. The main challenges in satellite image registration are finding out the optimum transformation parameters. Here in this work the non-alignment parameters are considered to be rigid and affine transformation. An intensity based satellite image registration technique is being used to register the floating image to the native co-ordinate system where the normalized mutual information (NMI) is taken as the similarity metric for optimizing and updating transform parameters. Because of no assumptions are made regarding the nature of the relationship between the image intensities in both modalities NMI is very general and powerful and can be applied automatically without prior segmentation on a large variety of data and as well works better for overlapped images as compared to mutual information(MI). In order to get maximum accuracy of registration the NMI is optimized using Genetic algorithm, particle swarm optimization and hybrid GA-PSO. The random initialization and computational complexity makes GA oppressive, whereas weak local search ability with a premature convergence is the main drawback of PSO. Hybrid GA-PSO makes a trade-off between the local and global search in order to achieve a better balance between convergence speed and computational complexity. The above registration algorithm is being validated with several satellite data sets. The hybrid GA-PSO outperforms in terms of optimized NMI value and percentage of mis-registration error.
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Parida, Satyabrata. "Denoising Of Satellite Images." Thesis, 2014. http://ethesis.nitrkl.ac.in/6612/1/Satyabrata_Parida_PROJECT_THESIS.pdf.
Full textAbstract:
We use images in our day to day life for keeping a record of information or merely to convey a message. There are a number of parameters which determine the quality of an image or a photograph most of which cannot be solved manually without the help of a computer whatsoever
any image that has been captured represents a deteriorated version of the original image. However
its clear that by any means we can never get the ideal image which is hypothetical as it is 100% accurate which is not possible. Our aim in image processing is to get the best possible image with minimum number of errors. In order to come to the conclusion of a certain task the correction of this deteriorated version is of optimal importance. Rectifying too much lighting effects, instance noising, geometrical faults, unwanted colour variations and blur are some of the important
parameters we need to attend to in order to get a good and useful image. In this paper, the deterioration of images because of noising has been addressed. Noise is any undesired information which adversely affects the quality and content of our image. Primary factors responsible for creating noise in an image are the medium through which photograph is taken (climatic and
atmospheric factors like pressure and temperature), the accuracy of the instrument used to take the photograph (for instance camera) and the quantization of data used to store the image.
This noise can be removed by an image processing technique called Image restoration. Image restoration process is concerned with the reconstruction of the original image from a noisy one.That is it tries to perform an operation on the image as the inverse of the imperfections in the image formation system. Degraded image can be perfected by various processes which are actually the reverse of noising. These filtering techniques are very simple and can be applied very easily through software. Some filtering processes have better performance than the others. This depends on the type of noise the image has. These filters are used in a variety of applications efficiently in preprocessing module. In this paper, the restoration performance of Arithmetic mean filter, Geometric mean filter and Median filter have been analyzed. The performance of these filters is analyzed by applying it on satellite images which are affected by Impulse noise, Speckle noise and Gaussian
noise. Since the satellite images are being corrupted by various noises, the satellite images are considered in this paper to analyze the performance of arithmetic mean filter, geometric mean filter and median filter.
By observing the obtained results and PSNR value for various satellite images under different
noises, we have recorded the following conclusion.
• the median filter gives better performance for satellite images affected by impulse noise than
arithmetic mean filter and geometric mean filter.
•the arithmetic mean filter gives better performance for gaussian noise than median filter and
geometric mean filters for all satellite images.
•the arithmetic mean filter gives better performance for speckle noise than median filter and
geometric mean filter for all satellite images.
Median Filter is an image filter that is more effective in situations where white spots and black
spots appear on the image. For this technique the middle value of the m×n window is considered to
replace the black and white pixels.After white spots and black spots appear on the image, it
becomes pretty difficult to find which pixel is the affected pixel. Replacing those affected pixels
with AMF, GMF and HMF is not enough because those pixels are replaced by a value which is not
appropriate to the original one. It is observed that the median filter gives better performance than
AMF and GMF for distorted images. The performance of restoration filter can be increased further
to completely remove noise and to preserve the edges of the image by using both linear and
nonlinear filter together.
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Juen, Wen-Quei, and 鄭文貴. "A study of satellite image fusion." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/15965612633911738509.
Full textAbstract:
碩士元智大學
資訊工程學系
97
The IKONOS satellite produce a PAN image (high-resolution panchromatic) and 4 MS images (low-resolution multispectral) simultaneously. In this thesis, an image fusion method is proposed to fuse the PAN and MS images. In the proposed method, not only the edge information in the PAN image but the spectral information in the MS images can be preserved. In the proposed fusion method, ANN (Artificial Neural Network) is used to optimize the fusion parameters which involve the parameter adjustment with BT (Brovey Transform sharpening method) and HIS (intensity-hue-saturation transform method) HIS (intensity-hue-saturation transform method). Based on the simulation results obtained in this study, the proposed fusion method indeed improves the perceptual quality in both edge and spectral information
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Liu, Ming-Che, and 劉銘哲. "Study on Image Matching and Registration for Different Resource Satellite Images." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/964p5j.
Full textAbstract:
碩士國立中央大學
土木工程研究所
96
Image registration is a key issue in many image processing applications in remote sensing. Examples of these applications include change detection using multiple images acquired at different times, and fusion of image data from multiple sensor types. SIFT (Scale Invariant Feature Transform), Canny edge detector and Least-squares matching method are proposed in this research. At first, the initial matching point pairs are detected from manual adjustment or the SIFT algorithm, which is invariant to image scale and rotation. And then, edges in both images are located by using the Canny algorithm and petty contours are cleaned by a bounding-box. Furthermore, more matching point pairs are selected using a cost function that measures the gradient orientation and distance between all possible pairs of the points. Pairing image windows are built and segmented to get radiometric parameters, and the radiometric parameters are used here to modulate the slave image window. Finally, master image window and modulated slave image window are matched by least-squares matching, and control points are found. The Thin-Plate Splines (TPS) method is used to register master and slave images. Experimental results show that numerous matching points can be obtained correctly and automatically, and different satellite images can be registered precisely.
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Bethke, William J. "Accuracy of satellite data navigation." Thesis, 1988. http://hdl.handle.net/10945/22850.
Full textAbstract:
Approved for public release; distribution is unlimitedImage navigation is critical to the effective use of digital imagery for meteorological and oceanographic studies. This thesis reviews various methods used to navigate imagery to the earth and investigates the accuracy of the Naval Postgraduate School (NPS) model. An explanation of how the NPS navigation process works is included for completeness. Results from 2 2 separate runs of the NPS model are studied.
http://archive.org/details/accuracyofsatell00beth
Captain, United States Marine Corps
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Chu, Yi-Ching, and 朱怡靜. "Applying Satellite Image to Landuse Change Analysis." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/29414939409768742729.
Full textAbstract:
碩士國立宜蘭大學
土木工程學系碩士班
96
Ever since the government began promoting industrialization, Taiwan’s economy has seen dramatic improvement and the national income has increased substantially. The overall economic growth has been greatly restricted by usable land and focused in the northern area. Although I-Lan is considered to be part of the northern area, due to poor terrain and inadequate transportation limitations, the economic growth has been slow and does not compare to the growth experienced by other cities in the northern area. To facilitate the transportation of traffic going in and out of I-Lan, the government constructed National Highway 5 and improved I-Lan inter-city roads to connect bordering towns and counties. This will also help balance out the disproportional growth between the city and the countryside. Using FORMOSAT2 satellite imagery from 2005 to 2007 and ground level research data. To penetrate Land Change Modeler and Image Differencing and so on different methods of change detect. Discussing land use change situation in I-Lan, use the satellite imagery after NDVI and Image Differencing. We see that after I-Lan’s transportation was improved, although the population decreased, new building construction is starting to shift beyond the outskirts of the city.
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Tsung, Hui-Tzu, and 叢蕙滋. "Satellite Image Registration Using Feature-Based Matching." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/82046843605361313892.
Full textAbstract:
碩士國立中央大學
土木工程學系
85
In order to enhance the applicability for satellite remote sensing data, imageintegration is getting important. The geometric registration between images is an indepensable step in the data integration. Based on the feature matching techniques, this investigation strives to increasethe degree of automation in image registration. Using the boundary of homogeneous patch as a featuer, a shape descriptor, i.e.,Shape Matrix(SM), is established. Then a matching procedure follows. According to the corresponding patches, the pair os centroids are used to perform the coordinates transformation between a reference image and its counterpart, i.e., sensed image. Thus fine matching for image registration control points may be performed. The major work of this investi-gation comprises two parts, namely, initial correspondence and image registration.The feature matching is applied for initial correspondence. the procedure includes:(1) building up shape matrices for feature polygons extracted form images with homo-geneous reference. Then a similarity assessment follows to produce potential corres-ponding pairs. (2)considering the geometrical and topological relationship, unreliablematched are excluded. In the image registration part, area-based matching is appllied to fine tune the initial matching. The conjugate point pairs are then used to constructthe triangulated networks for further piecewise registration.
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Chang, Chih-Ching, and 張智菁. "A Study on Satellite Image Target Recognization." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/50999739065642100506.
Full textAbstract:
碩士國防大學理工學院
電子工程碩士班
99
With improving technology of remote sensing, the top view images viewed from sky can be easily obtained by satellite and ready to provide as a key clue to a variety of applications. Due to the satellite image embedded with such abundant information, how to interpret and explain the content of image has already become an important technology in the field of national defense. In order to overcome these problems, the main research in this thesis is to establish the technologies about how to segment and recognize the satellite objects. First, in order to reach a better result, we have devised the image pre-processing algorithms to trim the satellite image. The pre-process in this stage include the conversion between BMP and RAW format and color space transformation. Second, we apply the Otsu edge detection, PCT (Principal Component Transform) and K-mean methods on the test image and then conduct the binary image comparison. Third, we use the Global Three Step Search (GTSS) method to find the distinguished objects. The search method is similar to the popular Three Step Search (TSS) algorithm used in video compression. We use GTSS to find the candidate objects in the image. After the desired objects have been extracted, we then calculate its Zernike moments to obtain their respective characteristics. With the obtained features, we then compute the distance between the query and database image. Zernike Moment is robust to several geometric operations such as the scaling, object rotating and shifting. With the proposed algorithms, we can successfully extract the distinguish objects from the satellite image. Then the system proceeds to recognize these important objects. The recognition rate is reached 75% with our algorithm.
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I-ChenTSAI and 蔡易澄. "Adaptive Contrast Enhancement for Satellite Image Registration." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/42uqq3.
Full textAbstract:
碩士國立成功大學
測量及空間資訊學系
106
Remote sensing researches using optical satellite images have been addressed for years. Image matching is one of the key techniques applied to remote sensing applications. For instance, image fusion and orthogonal image generation require the step of image matching in data processing. This study focuses on image matching for optical satellite images. The goal is to improve matching results in terms of algorithm robustness and the number of matched feature pairs. The matching algorithm proposed in this study is based on Speed Up Robust Feature (SURF). The feature points are detected and encoded by SURF algorithm. Furthermore, the matching feature points in input images are matched using their encoded descriptions. Algorithm efficiency and robustness are the advantages of SURF algorithm. However, detailed features in near-homogenous regions of the satellite images may not be successfully detected because of the inefficient spatial resolution of satellite images. In this study, a local image enhancement is performed prior to the feature detection. Image contrast adjustment of different degrees produces an image ranking in the order of image contrast. This image ranking is further processed by SURF algorithm. The image rank will have the achievement about the sum of the contrast image rank’s matching results. From quantitative and qualitative analyses, image matching with the proposed local image enhancement improve the matching results, in terms of matching accuracy and number of matched pairs.
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KHANNA, CHINTAN. "SATELLITE IMAGE CONTRAST ENHANCEMENT USING MODIFIED HISTOGRAM." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15235.
Full textAbstract:
This project presents a study of various Histogram Equalisation based Contrast Enhancement (CE) techniques followed by the proposal of a novel CE algorithm for satellite and aerial images. The algorithm is referred as Contour Based Histogram Equalisation (CBHE). The algorithm presents a novel method to capture the structural property of an image using the contour of the image. The algorithm addresses the inherent drawbacks of HE viz. artefacts and saturation by decreasing the contribution of high probability pixels in the histogram and increasing that of low probability pixels. Finally the algorithm enhances the features of the image by adjusting the coefficients of DCT. The algorithm generates good contrast images with richer details over a varied set of images including satellite and aerial images. It is computationally comparable to HE, does not introduce noise and saturation, preserves characteristic shape of original image histogram and does not enhance an already high contrast image. It thus qualifies as an effective pre-processing step. CBHE is compared with the conventional and best of HE based techniques both quantitatively and visually. The quantitative analysis of the results is carried out using several standard measures like Discrete Entropy, Signal to Noise Ratio (PSNR), Measurement of Enhancement (EME), Average Mean brightness Error (AMBE), Gradient Magnitude Similarity Index (GMSD) and Structural Similarity Index (SSI) over varied datasets.