Relevant bibliographies by topics / Satellite Image

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Satellite Image'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Satellite Image"

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Zeng, Tao, Lijian Shi, Lei Huang, Ying Zhang, Haitian Zhu, and Xiaotong Yang. "A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica." Remote Sensing 15, no. 18 (September 7, 2023): 4399. http://dx.doi.org/10.3390/rs15184399.

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Antarctic mapping with satellite images is an important basic task for polar environmental monitoring. Since the first Chinese marine satellite was launched in 2002, China has formed three series of more than 10 marine satellites in orbit. As global operational monitoring satellites of ocean color series, HY-1C and HY-1D have good coverage characteristics and imaging performance in polar regions, and they provide an effective tool for Antarctic monitoring and mapping. In this paper, Antarctic images acquired by the HY-1 satellite Coastal Zone Imager (CZI) sensor were used to study color matching in the mosaic process. According to the CZI characteristics for Antarctic imaging, experiments were carried out on the illuminance nonuniformity of a single image and color registration of multiple images. A gray-level segmentation color-matching method is proposed to solve the problem of image overstretching in the Antarctic image color-matching process. The results and statistical analysis show that the proposed method can effectively eliminate the color deviation between HY-1 Antarctic images, and the mosaic results have a good effect.
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Manocha, Neetu, and Rajeev Gupta. "A Comparative Analysis of Existing Satellite Image Enhancement Techniques for Effective Visual Display." Journal of Computational and Theoretical Nanoscience 16, no. 9 (September 1, 2019): 4003–7. http://dx.doi.org/10.1166/jctn.2019.8285.

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Due to environment untidiness and inappropriate setting or dealing of camera, a satellite image contains blur or other types of noises. These images are captured by satellites consist lots of information about the surface of earth or other planets. But, due to blur or noise, the quality of these images is degraded. Now days, there are many fields in which satellite images are used, which effects the environment. The accuracy and effective visual display of satellite images with high image resolution using CBIR technique is major concern. This paper presents a comparative analysis of existing satellite image enhancement techniques to reduce the blur of an image on the basis of accuracy and response time. The aim of research work is to eliminate the noise without losing high frequency details and to enhance the image for effective visual display.
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Gasmi, Anis, Cécile Gomez, Abdelghani Chehbouni, Driss Dhiba, and Hamza Elfil. "Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches." Remote Sensing 14, no. 5 (February 24, 2022): 1103. http://dx.doi.org/10.3390/rs14051103.

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Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands’ images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.
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Shi, Qi, Daheng Wang, Wen Chen, Jinpei Yu, Weiting Zhou, Jun Zou, and Guangzu Liu. "Research on Spaceborne Target Detection Based on Yolov5 and Image Compression." Future Internet 15, no. 3 (March 19, 2023): 114. http://dx.doi.org/10.3390/fi15030114.

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Satellite image compression technology plays an important role in the development of space science. As optical sensors on satellites become more sophisticated, high-resolution and high-fidelity satellite images will occupy more storage. This raises the required transmission bandwidth and transmission rate in the satellite–ground data transmission system. In order to reduce the pressure from image transmission on the data transmission system, a spaceborne target detection system based on Yolov5 and a satellite image compression transmission system is proposed in this paper. It can reduce the pressure on the data transmission system by detecting the object of interest and deciding whether to transmit. An improved Yolov5 network is proposed to detect the small target on the high-resolution satellite image. Simulation results show that the improved Yolov5 network proposed in this paper can detect specific targets in real satellite images, including aircraft, ships, etc. At the same time, image compression has little effect on target detection, so detection complexity can be effectively reduced and detection speed can be improved by detecting the compressed images.
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Yeole, Aditya. "Satellite Image Dehazing." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 5184–92. http://dx.doi.org/10.22214/ijraset.2023.52728.

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Abstract: The images captured during haze, murkiness and raw weather has serious degradation in them. Image dehazing of a single image is a problematic affair. While already-in-use systems depend on high-quality images, some Computer Vision applications, such self-driving cars and image restoration, typically use input from data that is of poor quality.. This paper proposes a deep CNN model based on dehazing algorithm using U-NET, dynamic U-NET and Generative Adversarial Networks (CycleGANs). CycleGAN is a method that comprehends automatic training of image-to-image transformation without associated examples. To train the model network, we use SIH dataset as the training set. The superior performance is accomplished using appreciably small dataset, the corresponding outcomes confirm the adaptability and strength of the model.
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Park, Daesoon, Doochun Seo, and Heeseob Kim. "KOMPSAT Optical Image Data Provision and Quality Management." GEO DATA 4, no. 4 (December 31, 2022): 28–38. http://dx.doi.org/10.22761/dj2022.4.4.004.

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Korea Aerospace Research Institute (KARI) is conducting continuous quality control to provide reliable optical image products to various users. This paper describes KOrea Multi-Purpose SATellites (KOMPSAT-3 and KOMPSAT-3A) characteristics, operation, and image collection mode in order to enhance satellite image application. Also, image product of the satellites and quality management of the image product are described in this paper. The KOMPSAT-3 launched in 2012 and KOMPSAT-3A launched in 2015 collected many imageries around the world and provide them to users through web. Users can search for images through web catalog and order new imaging task. The KOMPSAT images provided under the KARI control is expected to be great help for earth observation and satellite image application enhancement.
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Jain, Geerisha. "Satellite Image Processing Using Fuzzy Logic and Modified K-Means Clustering Algorithm for Image Segmentation." Computational Intelligence and Machine Learning 3, no. 2 (October 14, 2022): 57–61. http://dx.doi.org/10.36647/ciml/03.02.a008.

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Satellite images are useful in providing a real time dynamic picture of the earth and its environment. The large assemblage of remote sensing satellites orbiting the earth provide an extensive and periodic coverage of the planet through the capture of live images round the clock, in turn enabling numerous uses for the benefit of mankind. In the field of satellite image processing, image segmentation is one of the vital steps for extracting and gathering huge amount of information from the satellite images. The basic k-means clustering algorithm is simple and fast in terms of dealing with the required segmentation, but the limitation associated with this clustering is its inability to produce the same result for every run, as the resulting clusters depends on the initial random assignments. In this paper, an enhanced modified k-means clustering algorithm is proposed for the effective segmentation of the satellite images with an objective to overcome the demerits of the traditional k-means by combining fuzzy logic with the membership function. The proposed methodology continuously produces the same result for each run. As an outcome, the experimental results proved that the enhanced k-means algorithm is an effective and more efficient process for the precise and accurate segmentation of satellite images. Index Terms : Image Segmentation, Satellite Imagery, Fuzzy logic, K-Means, Clustering.
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Donguy, Patrick. "Sable ou poussière ? Image satellitale, Image-satellite ou image satellitaire ?" La Météorologie, no. 23 (1998): 89. http://dx.doi.org/10.4267/2042/54524.

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Govindarajulu, S. "Image Registration on Satellite Images." IOSR Journal of Electronics and Communication Engineering 3, no. 5 (2012): 10–17. http://dx.doi.org/10.9790/2834-0351017.

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Jacobsen, K. "WHICH SATELLITE IMAGE SHOULD BE USED FOR MAPPING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 827–34. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-827-2023.

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Abstract. Today, topographical mapping based on satellite images is a standard method. With the large number of very high-resolution optical satellites, it only a question of the Ground Sampling Distance (GSD) and the map scale to be generated. But the classical large-format satellite images are expensive. With the today’s variety of the classical small satellites (601kg to 1200kg) to Nano-satellites (1.1kg to 10kg) of 3U (10cm × 10cm × 30cm), various options are available that influence the economic solutions. An overview of the accessible optical satellites is given, with some specific information on the mini-satellites that offer new economical solutions for topographic mapping. Significantly more optical satellites are currently in operation, but their images are used only for military purposes or they are restricted for national use due to lack of image storage and limited download possibilities.
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Dissertations / Theses on the topic "Satellite Image"

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Bassett, Robert M. "Automated satellite image navigation." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23552.

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This 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 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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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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Books on the topic "Satellite Image"

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Bassett, Robert M. Automated satellite image navigation. Monterey, Calif: Naval Postgraduate School, 1992.

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Ünsalan, Cem, and Kim L. Boyer. Multispectral Satellite Image Understanding. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-667-2.

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SanFilipo, John R. Satellite image maps of Pakistan. [Reston, VA: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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(Firm), SPOT Image, ed. Spot Image. Toulouse: Spot Image, 1988.

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1942-, Burch J. L., ed. Magnetospheric imaging: The image prime mission. Dordrecht: Kluwer Academic Publishers, 2003.

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Borra, Surekha, Rohit Thanki, and Nilanjan Dey. Satellite Image Analysis: Clustering and Classification. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6424-2.

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Consortium, Maryland Space Grant, ed. An introduction to satellite image interpretation. Baltimore: Johns Hopkins University Press, 1997.

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Farouk, El-Baz, and Boston University. Center for Remote Sensing., eds. Wadis of Oman: Satellite image atlas. London: Stacey International, 2002.

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Hemanth, D. Jude, ed. Artificial Intelligence Techniques for Satellite Image Analysis. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-24178-0.

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European "International Space Year" Conference (1992 Munich, Germany). Navigation & mobile communications image processing, GIS & space-assisted mapping. Paris, France: European Space Agency, 1992.

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Book chapters on the topic "Satellite Image"

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Borra, Surekha, Rohit Thanki, and Nilanjan Dey. "Satellite Image Clustering." In Satellite Image Analysis: Clustering and Classification, 31–52. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6424-2_3.

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Borra, Surekha, Rohit Thanki, and Nilanjan Dey. "Satellite Image Classification." In Satellite Image Analysis: Clustering and Classification, 53–81. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6424-2_4.

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Neteler, Markus, and Helena Mitasova. "Satellite Image Processing." In The Kluwer International Series in Engineering and Computer Science, 207–62. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3578-9_9.

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Berger, Zeev. "Digital Image Manipulation." In Satellite Hydrocarbon Exploration, 35–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78587-0_2.

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Berger, Zeev. "Image Interpretation Techniques: Exposed Structures." In Satellite Hydrocarbon Exploration, 53–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78587-0_3.

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Herlin, Isabelle, Dominique Béréziat, and Nicolas Mercier. "Recovering Missing Data on Satellite Images." In Image Analysis, 697–707. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_65.

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Flitti, Farid, Mohammed Bennamoun, Du Huynh, Amine Bermak, and Christophe Collet. "Probabilistic Satellite Image Fusion." In Computer Analysis of Images and Patterns, 410–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_50.

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Ünsalan, Cem, and Kim L. Boyer. "Introduction." In Multispectral Satellite Image Understanding, 1–4. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-667-2_1.

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Ünsalan, Cem, and Kim L. Boyer. "Detecting Residential Regions by Graph-Theoretical Measures." In Multispectral Satellite Image Understanding, 131–36. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-667-2_10.

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Ünsalan, Cem, and Kim L. Boyer. "Review on Building and Road Detection." In Multispectral Satellite Image Understanding, 139–44. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-667-2_11.

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Conference papers on the topic "Satellite Image"

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Rahmadi, Deddy, and Silvia Rachmawati. "Landsat Satellite Image Quality Improvement Using Discrete Cosine Transform Method." In The 6th International Conference on Science and Engineering. Switzerland: Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-bvfs09.

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Landsat satellite images are images that represent the ocean and land areas of the earth. Image data can be used for various purposes such as environmental analysis, remote sensing, mapping, and others. However, the quality of Landsat imagery is often unsatisfactory due to interference or noise from sources such as sensors, transmission, atmosphere, and storage. Therefore, they can reduce the contrast, sharpness, and information of landsat satellite images. Some of these disturbances prevent people from obtaining clear geographical locations. In order to overcome this problem, an effective and efficient method of Landsat satellite image quality improvement is needed. This research uses an image improvement method, namely discrete cosine transformation. The discrete cosine transformation method is used to reduce image noise by dividing it into each basic element. The method can perform the calculation process metematically and applicatively in the process of Landsat satellite image improvement. The processed results obtained are used to design and implement Landsat satellite image enhancement using the discrete cosine transformation method.
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Schouten, Theo E., and Maurice S. Klein Gebbinck. "Quality measures for image segmentation using generated images." In Satellite Remote Sensing II, edited by Jacky Desachy. SPIE, 1995. http://dx.doi.org/10.1117/12.226860.

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Vani, K. "Satellite image processing." In 2017 Fourth International Conference on Signal Processing,Communication and Networking (ICSCN). IEEE, 2017. http://dx.doi.org/10.1109/icscn.2017.8085410.

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Hatton, Conner J., and Jesse J. Adams. "Satellite Image Algorithms." In UQ24 - SIAM Conference on Uncertainty Quantification Feb. 27-Mar. 1, 2024 - Trieste, Italy https://www.siam.org/conferences/cm/conference/uq24. US DOE, 2024. http://dx.doi.org/10.2172/2318478.

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Schouten, Theo E., Maurice S. Klein Gebbinck, Ron P. Schoenmakers, and Graeme G. Wilkinson. "Finding thresholds for image segmentation." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196706.

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Fitch, J. P., T. W. Lawrence, D. M. Goodman, and E. M. Johansson. "Speckle Imaging of Satellites." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/srs.1992.wa1.

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We performed a series of experiments using the Air Force Maui Optical Station’s 1.6 m telescope and a bare CCD detector to capture speckle images of various satellites. The speckle images were processed with bispectral techniques for recovering image Fourier phase as well as projection onto convex sets for recovering image Fourier magnitude from the projected autocorrelation. Results of imaging point stars and binaries are shown as a baseline assessment of our technique. We have reconstructed high quality images of numerous satellites and will show reconstructions of a very familiar satellite: the Hubble Space Telescope. To our knowledge, this is the first demonstration of the use of bare CCDs for speckle imaging of relatively bright objects such as artificial satellites.
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Roux, Ludovic. "Multisources approach for satellite image interpretation." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196714.

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Yu, Shan, Gerard Giraudon, and Marc Berthod. "Integrating map knowledge in satellite image analysis." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196707.

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Moissinac, Henri, Henri Maitre, and Isabelle Bloch. "Urban aerial image understanding using symbolic data." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196729.

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Marthon, Philippe, Bruno Paci, and Eliane Cubero-Castan. "Finding the structure of a satellite image." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196767.

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Reports on the topic "Satellite Image"

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Earth Data Analysis Center, Earth Data Analysis Center. Satellite image of New Mexico. New Mexico Bureau of Geology and Mineral Resources, 2000. http://dx.doi.org/10.58799/rm-23.

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Wachs, Brandon. Satellite Image Deep Fake Creation and Detection. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1812627.

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Groeneveld, Davis, and Williams. L51974 Automated Detection of Encroachment Events Using Satellite Remote Sensing. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2002. http://dx.doi.org/10.55274/r0011300.

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As an integral part of the ongoing effort to develop an operational capability of remote sensing based pipeline encroachment monitoring, this investigation focused on the development of automated target detection using synthetic aperture radar (RADARSAT) and optical (QUICKBIRD, EROS) satellite imagery. Specifically, the study aimed at meeting the following objectives: To develop automated target detection algorithms for optical and radar imagery that replicate detection rates obtained through visual image interpretation; To investigate the utility of newly available high-resolution optical satellite imagery for encroachment monitoring; To reduce false alarms through the processing of multitemporal radar images; and To identify and prioritize areas of future research and development required for the operational application of the technology.
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Bissett, W. P. Web-Based Library and Algorithm System for Satellite and Airborne Image Products. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada540801.

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Bloomfield, R. A., and G. R. Dobson. Image-Data Transmission Demonstration over the Tracking and Data Relay Satellite System. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada352534.

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van der Sanden, J. J., P. W. Vachon, and J. F. R. Gower. Combining Optical and Radar Satellite Image Data for Surveillance of Coastal Waters. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219631.

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Du, Y., B. Guindon, and J. Cihlar. Haze detection and removal in high resolution satellite image with wavelet analysis. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219726.

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Bissett, W. P. A Web-Based Library and Algorithm System for Satellite and Airborne Image Products. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada541077.

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Du, Y., P. W. Vachon, and J. J. van der Sanden. Satellite image fusion with multi-scale wavelet analysis: Preserving Spatial Information and Minimizing Artifacts (PSIMA). Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219786.

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Guindon, B. Computer-based aerial image understanding: a review and assessment of its application to planimetric information extraction from very high resolution satellite images. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1997. http://dx.doi.org/10.4095/218537.

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