Relevant bibliographies by topics / Remote sensing Image processing Remote sensing Remote sensing Computer algorithms

Contents

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

Academic literature on the topic 'Remote sensing Image processing Remote sensing Remote sensing Computer algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Remote sensing Image processing Remote sensing Remote sensing Computer algorithms"

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Li, Hongchao, and Fang Wu. "Conversion and Visualization of Remote Sensing Image Data in CAD." Computer-Aided Design and Applications 18, S3 (October 20, 2020): 82–94. http://dx.doi.org/10.14733/cadaps.2021.s3.82-94.

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In this paper, a process visualization model for remote sensing image classification algorithms is constructed to analyze the current processing characteristics of process visualization in remote sensing application systems. The usability of the model is verified in a remote sensing application system with a remote sensing image classification algorithm based on support vector machines as an example. Given the characteristics of remote sensing applications that require high visualization process and a large amount of data processing, the basic process of an image classification algorithm for remote sensing applications is summarized by analyzing the basic process of existing image classification algorithms in remote sensing applications, taking into account the characteristics of process visualization. Based on the existing process of remote sensing image classification algorithm, a process visualization model is proposed. The model takes a goal-based process acts as the basic elements of the model, provides visualization functions and interfaces for human-computer interaction through a human-computer interaction selector, and uses a template knowledge base to save processing data and realize the description of customized processes. The model has little impact on the efficiency and accuracy of the support vector machine-based remote sensing image classification algorithm during the process of process visualization and customization. Finally, the application of the model to integrate business processing of earth observation can address the problem of process customization visualization for remote sensing applications to some extent.
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Zhu, Zhiqin, Yaqin Luo, Hongyan Wei, Yong Li, Guanqiu Qi, Neal Mazur, Yuanyuan Li, and Penglong Li. "Atmospheric Light Estimation Based Remote Sensing Image Dehazing." Remote Sensing 13, no. 13 (June 22, 2021): 2432. http://dx.doi.org/10.3390/rs13132432.

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Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.
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Li, J., J. Sheng, Y. Chen, L. Ke, N. Yao, Z. Miao, X. Zeng, L. Hu, and Q. Wang. "A WEB-BASED LEARNING ENVIRONMENT OF REMOTE SENSING EXPERIMENTAL CLASS WITH PYTHON." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2020 (August 24, 2020): 57–61. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2020-57-2020.

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Abstract. Remote sensing course is a general disciplinary required course of human geography and urban-rural planning major. Its class hour is 48, including theoretical classes and experimental classes. Rapid technological developments is remote sensing area demand quick and steady changes in the education programme and its realization, especially in experimental classes. Experimental classes include: introduction to remote sensing software and basic operations, remote sensing data pre-processing (input, output, 2D and 3D terrain display, image cut, image mosaic, and projection transformation), remote sensing image enhancement, remote sensing image transformation, computer aided classification, image interpretation, and remote sensing image terrain analysis. There are two difficulties in the remote sensing experimental classes. First, it cost a lot of time to prepare the remote sensing software and the remote sensing images. Second, some students just want to use the remote sensing as a tool to investigate environment changing, some other students may want to study more remote sensing image processing technologies. A web-based learning environment of remote sensing is developed to facilitate the application of remote sensing experimental teaching. To make the learning more effective, there are eight modules including four optional modules. The Python programming language is chosen to implement the web-based remote sensing learning environment. The web-based learning environment is implemented in a local network server, including the remote sensing data processing algorithms and many satellite image data. Students can easily exercise the remote sensing experimental courses by connecting to the local network server. It is developed mainly for remote sensing experimental course, and also can be adopted by digital image processing or other courses. The feature of web-based learning may be very useful as the online education adopted because of Corona Virus Disease 2019. The results are encouraging and some recommendations will be extracted for the future.
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Jin, Liang, and Guodong Liu. "An Approach on Image Processing of Deep Learning Based on Improved SSD." Symmetry 13, no. 3 (March 17, 2021): 495. http://dx.doi.org/10.3390/sym13030495.

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Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.
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Tripathi, Rakesh, and Neelesh Gupta. "A Review on Segmentation Techniques in Large-Scale Remote Sensing Images." SMART MOVES JOURNAL IJOSCIENCE 4, no. 4 (April 20, 2018): 7. http://dx.doi.org/10.24113/ijoscience.v4i4.143.

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Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.
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Zou, Quan, Guoqing Li, and Wenyang Yu. "Cloud Computing Based on Computational Characteristics for Disaster Monitoring." Applied Sciences 10, no. 19 (September 24, 2020): 6676. http://dx.doi.org/10.3390/app10196676.

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Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance.
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Zhao, Ying, and Ye Cai Guo. "Remote Sensing Image Enhancement Based on Wavelet Transformation." Applied Mechanics and Materials 198-199 (September 2012): 223–26. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.223.

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The contrast of remote sensing images is very low, which include various noises. In order to make full used of remote sensing image information extraction and processing, the original image should have to be enhanced. In this paper the enhancement algorithm based on the biothogonal wavelet transform is proposed. Firstly, we have to eliminate the beforehand noise, and then take advantage of the non-linear wavelet transform to enhanced low-frequency and high- frequency coefficient respectively. Finally, the new picture is reconstruct by the transformed low-frequency and high-frequency coefficient. The efficiency of the proposed algorithm was proved by the theoretical analysis and computer simulations.
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Xu, R. G., G. Qiao, Y. J. Wu, and Y. J. Cao. "EXTRACTION OF RIVERS AND LAKES ON TIBETAN PLATEAU BASED ON GOOGLE EARTH ENGINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1797–801. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1797-2019.

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<p><strong>Abstract.</strong> Tibetan Plateau (TP) is the most abundant area of water resources and water energy resources in China. It is also the birthplace of the main rivers in Southeast Asia and plays an important strategic role. However, due to its remote location and complex topography, the observation of surface hydrometeorological elements is extremely scarce, which seriously restricts the understanding of the water cycle in this area. Using remote sensing images to extract rivers and lakes on TP can obtain a lot of valuable water resources information. However, the downloading and processing of remote sensing images is very time-consuming, especially the processing of remote sensing images with large-scale and long time series often involves hundreds of gigabytes of data, which requires a high level of personal computers and is inefficient. As a cloud platform dedicated to data processing and analysis of geoscience, Google Earth Engine(GEE) integrates many excellent remote sensing image processing algorithms. It does not need to download images and supports online remote sensing image processing, which greatly improves the output efficiency. Based on GEE, the monthly data of Yarlung Zangbo River at Nuxia Hydrological Station and the annual data of typical lakes were extracted and vectorized from the pre-processed Landsat series images. It was found that the area of Yarlung Zangbo River at Nuxia Hydrological Station varies periodically. The changing trend of typical lakes is also revealed.</p>
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Wang, Sen, Xiaoming Sun, Pengfei Liu, Kaige Xu, Weifeng Zhang, and Chenxu Wu. "Research on Remote Sensing Image Matching with Special Texture Background." Symmetry 13, no. 8 (July 29, 2021): 1380. http://dx.doi.org/10.3390/sym13081380.

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The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.
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Kaur, Sumit. "Deep Learning Based High-Resolution Remote Sensing Image classification." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 10 (October 30, 2017): 22. http://dx.doi.org/10.23956/ijarcsse.v7i10.384.

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Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification.
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Dissertations / Theses on the topic "Remote sensing Image processing Remote sensing Remote sensing Computer algorithms"

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Cisz, Adam. "Performance comparison of hyperspectral target detection algorithms /." Online version of thesis, 2006. https://ritdml.rit.edu/dspace/handle/1850/3020.

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Wang, Zhen. "Modeling wildland fire radiance in synthetic remote sensing scenes /." Online version of thesis, 2007. http://hdl.handle.net/1850/5787.

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Ientilucci, Emmett J. "Hyperspectral sub-pixel target detection using hybrid algorithms and physics based modeling /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1185.

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Doster, Timothy J. "Mathematical methods for anomaly grouping in hyperspectral images /." Online version of thesis, 2009. http://hdl.handle.net/1850/11575.

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Schuetter, Jared Michael. "Cairn Detection in Southern Arabia Using a Supervised Automatic Detection Algorithm and Multiple Sample Data Spectroscopic Clustering." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1269567071.

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Shah, Vijay Pravin. "A wavelet-based approach to primitive feature extraction, region-based segmentation, and identification for image information mining." Diss., Mississippi State : Mississippi State University, 2007. http://library.msstate.edu/etd/show.asp?etd=etd-07062007-134150.

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Story, Michael Haun. "Comparison of accuracy and efficiency of five digital image classification algorithms." Thesis, This resource online, 1987. http://scholar.lib.vt.edu/theses/available/etd-04122010-083611/.

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Li, Feng Engineering &amp Information Technology Australian Defence Force Academy UNSW. "Development of super resolution techniques for finer scale remote sensing image mapping." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/44098.

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In this thesis, methods for achieving finer scale multi-spectral classification through the use of super resolution (SR) techniques are investigated. A new super resolution algorithm Maximum a Posteriori based on the universal hidden Markov tree model (MAP-uHMT) is developed which can be applied successfully to super-resolve each multi-spectral channel before classification by standard methods. It is believed that this is the first time that a true super resolution algorithm has been applied to multi-spectral classification, and results are shown to be excellent. Image registration is an important step for SR in which misalignment can be measured for each of many low resolution images; therefore, a new and computationally efficient image registration is developed for this particular application. This improved elastic image registration method can deal with a global affine warping and local shift translations based on coarse to fine pyramid levels. The experimental results show that it can provide good registration accuracy in less computational time than comparable methods. Maximum a posteriori (MAP) is adopted to deal with the ill-conditioned problem of super resolution, wherein a prior is constructed based on the universal hidden Markov tree (uHMT) model in the wavelet domain. In order to test this prior for MAP estimation, it is first tested in the simpler and typically ill-conditioned problem of image denoising. Experimental results illustrate that this new image denoising method achieves good performance for the test images. The new prior is then extended to SR. By combining with the new elastic image registration algorithm, MAP-uHMT can super resolve both some natural video frames and remote sensing images. Test results with both synthetic data and real data show that this method achieves super resolution both visually and quantitatively. In order to show that MAPuHMT is also applicable more widely, it is tested on a sequence of long-range surveillance images captured under conditions of atmospheric turbulence distortion. The results suggest that super resolution may have been achieved in this application also.
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Van, der Westhuizen Lynette. "Concise analysis and testing of a software model of a satellite remote sensing system used for image generation." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/96029.

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Thesis (MEng) -- Stellenbosch University, 2014.
ENGLISH ABSTRACT: The capability of simulating the output image of earth observation satellite sensors is of great value, as it reduces the dependency on extensive field tests when developing, testing and calibrating satellite sensors. The aim of this study was to develop a software model to simulate the data acquisition process used by passive remote sensing satellites for the purpose of image generation. To design the software model, a comprehensive study was done of a physical real world satellite remote sensing system in order to identify and analyse the different elements of the data acquisition process. The different elements were identified as being the target, the atmosphere, the sensor and satellite, and radiation. These elements and a signature rendering equation are used to model the target-atmosphere-sensor relationship of the data acquisition process. The signature rendering equation is a mathematical model of the different solar and self-emitted thermal radiance paths that contribute to the radiance reaching the sensor. It is proposed that the software model be implemented as an additional space remote sensing application in the Optronics Sensor Simulator (OSSIM) simulation environment. The OSSIM environment provides the infrastructure and key capabilities upon which this specialist work builds. OSSIM includes a staring array sensor model, which was adapted and expanded in this study to operate as a generic satellite sensor. The OSSIM signature rendering equation was found to include all the necessary terms required to model the at-sensor radiance for a satellite sensor with the exception of an adjacency effect term. The equation was expanded in this study to include a term to describe the in-field-of-view adjacency effect due to aerosol scattering. This effect was modelled as a constant value over the sensor field of view. Models were designed to simulate across-track scanning mirrors, the satellite orbit trajectory and basic image processing for geometric discontinuities. Testing of the software model showed that all functions operated correctly within the set operating conditions and that the in-field-of-view adjacency effect can be modelled effectively by a constant value over the sensor field of view. It was concluded that the satellite remote sensing software model designed in this study accurately simulates the key features of the real world system and provides a concise and sound framework on which future functionality can be expanded.
AFRIKAANSE OPSOMMING: Dit is nuttig om ’n sagteware program te besit wat die gegenereerde beelde van ’n satellietsensor vir aarde-waarneming kan naboots. So ’n sagteware program sal die afhanklikheid van breedvoerige veldwerktoetse verminder gedurende die ontwerp, toetsing en kalibrasie fases van die ontwikkeling van ’n satellietsensor. Die doel van hierdie studie was om ’n sagteware model te ontwerp wat die dataverwerwingsproses van ’n passiewe satelliet afstandswaarnemingstelsel kan naboots, met die doel om beelde te genereer. Om die sagteware model te ontwerp het ’n omvattende studie van ’n fisiese regte wêreld satelliet afstandswaarnemingstelsel geverg, om die verskillende elemente van die dataverwerwingsproses te identifiseer en te analiseer. Die verskillende elemente is geïdentifiseer as die teiken, die atmosfeer, die sensor en satelliet, en vloed. Hierdie elemente, tesame met ’n duimdrukvergelyking, is gebruik om die teiken-atmosfeer-sensor verhouding van die dataverwerwingsproses te modelleer. Die duimdrukvergelyking is ’n wiskundige model van die verskillende voortplantingspaaie van gereflekteerde sonvloed en self-stralende termiese vloed wat bydra tot die totale vloed wat die sensor bereik. Dit is voorgestel dat die sagteware model as ’n addisionele ruimte afstandswaarnemingtoepassing in die ‘Optronics sensor Simulator’ (OSSIM) simulasie-omgewing geïmplementeer word. Die OSSIM simulasie-omgewing voorsien die nodige infrastruktuur en belangrike funksies waarop hierdie spesialis werk gebou kan word. OSSIM het ’n starende-skikking sensor model wat in hierdie studie aangepas is en uitgebrei is om as ’n generiese satellietsensor te funksioneer. Die OSSIM duimdrukvergelyking bevat al die nodige radiometriese terme, behalwe ’n nabyheids-verstrooiing term, om die vloed by die satellietsensor te modeleer. Die duimdrukvergelyking is uitgebrei in hierdie studie om ’n term in te sluit wat die verstrooiing van vloed vanaf naby-geleë voorwerpe, as gevolg van aerosol verstrooiing, kan beskryf. Die nabyheids-verstrooiing is gemodeleer as ’n konstante waarde oor die sigveld van die sensor. Modelle is ontwerp om die beweging van oor-baan skandering-spieëls en die satelliet wentelbaan trajek te bereken. ’n Basiese beeldverwerkings model is ook ontwerp om diskontinuïteite in geometriese vorms in die sensor beelde reg te stel. Toetsing van die sagteware model het gewys dat al die funksies korrek gefunksioneer het binne die limiete van die vasgestelde operasionele voorwaardes. Die toets resultate het ook bewys dat die in-sig-veld nabyheids-verstrooiing akkuraat gemodeleer kan word as ’n konstante waarde oor die sensor sigveld. Daar is tot die gevolgtrekking gekom dat die satelliet afstandswaarneming sagteware model wat in hierdie studie ontwerp is al die belangrikste kenmerke van die werklike wêreld stelsel kan simuleer. Die model vorm ’n beknopte en stewige raamwerk waarop toekomstige werk uitgebrei kan word.
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Lavalle, Marco. "Full and Compact Polarimetric Radar Interferometry for Vegetation Remote Sensing." Phd thesis, Université Rennes 1, 2009. http://tel.archives-ouvertes.fr/tel-00480972.

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Cette thèse aborde principalement le rôle que jouent les radars polarimétrique et interférométrique dans les applications de géosciences, tout particulièrement sur les forêts. Il est démontré que les modèles actuels simples de la corrélation spatiale des milieux naturels sont capables d'estimer de manière robuste la hauteur de la forêt et sa biomasse; lorsque la topographie est peu prononcée. La corrélation temporelle y est traitée plus précisément en définissant une fonction de corrélation temporelle dépendant de la hauteur de la canopée. Les effets de cette amélioration sur la modélisation directe et inverse sont discutés. Une expression simplifiée de ces modèles est proposée et validée dans le cas des basses fréquences. Nous utilisons à la fois des données polarimétriques satellitales, ainsi que des simulations numériques de rétrodiffusion afin d'illustrer les résultats. Pour les radars en polarimétrie compacte, la pseudo reconstruction est généralisée au cas interférométrique et son efficacité est démontrée seulement pour certaines combinaisons entre les composantes volumiques et les composantes du sol. Enfin, la qualité des données est abordée, en montrant que la rotation de Faraday peut être estimée et corrigée à partir des échos radar non focalisés et que les trièdres maillées peuvent servir d'étalonnage radiométrique des données à double polarisation.
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Books on the topic "Remote sensing Image processing Remote sensing Remote sensing Computer algorithms"

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Richards, John A. Remote Sensing Digital Image Analysis: An Introduction. 5th ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Theory of remote image formation. Cambridge, UK: Cambridge University Press, 2004.

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McClain, C. R. An analysis of GAC sampling algorithms: A case study. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1992.

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Cem, Ünsalan, and SpringerLink (Online service), eds. Two-Dimensional Change Detection Methods: Remote Sensing Applications. London: Springer London, 2012.

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Lasaponara, Rosa. Satellite Remote Sensing: A New Tool for Archaeology. Dordrecht: Springer Netherlands, 2012.

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Magaly, Koch, ed. Computer processing of remotely-sensed images: An introduction. 4th ed. Chichester, West Sussex, UK: Wiley-Blackwell, 2011.

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Watson, Kenneth. A 2D FFT filtering program for image processing with examples. [Denver, CO]: U.S. Dept. of the Interior, Geological Survey, 1992.

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Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.

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Canty, Morton John. Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.

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Canty, Morton John. Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL. 2nd ed. Boca Raton: Taylor & Francis, 2010.

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Book chapters on the topic "Remote sensing Image processing Remote sensing Remote sensing Computer algorithms"

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Ling, Han, Tao Fada, and Li Minglu. "Design and Implementation of the Image Processing Algorithm Framework for Remote Sensing." In Communications in Computer and Information Science, 479–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27452-7_65.

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Ma, Zhiqiang, and Wanwu Guo. "Remote Sensing Image Processing Using MCDF." In Lecture Notes in Computer Science, 454–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30133-2_59.

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Sawant, Neela, Sharat Chandran, and B. Krishna Mohan. "Retrieving Images for Remote Sensing Applications." In Computer Vision, Graphics and Image Processing, 849–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_76.

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Uma Shankar, B., Saroj K. Meher, Ashish Ghosh, and Lorenzo Bruzzone. "Remote Sensing Image Classification: A Neuro-fuzzy MCS Approach." In Computer Vision, Graphics and Image Processing, 128–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_12.

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Jiang, Chao, Ze-xun Geng, Xiao-feng Wei, and Chen Shen. "Research on Networked Integration Technology of Remote Sensing Image Processing." In Communications in Computer and Information Science, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34595-1_1.

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Wang, Xiaoyue, Zhenhua Li, and Song Gao. "Parallel Remote Sensing Image Processing: Taking Image Classification as an Example." In Communications in Computer and Information Science, 159–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34289-9_19.

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Ramos-Michel, Alfonso, Marco Pérez-Cisneros, Erik Cuevas, and Daniel Zaldivar. "A Survey on Image Processing for Hyperspectral and Remote Sensing Images." In Applications of Hybrid Metaheuristic Algorithms for Image Processing, 27–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40977-7_2.

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Wilkinson, Graeme G. "Recent Developments in Remote Sensing Technology and the Importance of Computer Vision Analysis Techniques." In Machine Vision and Advanced Image Processing in Remote Sensing, 5–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60105-7_1.

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Nalawade, Dhananjay B., Mahesh M. Solankar, Rupali R. Surase, Amarsinh B. Varpe, Amol D. Vibhute, Rajesh K. Dhumal, and Karbhari Kale. "Hyperspectral Remote Sensing Image Analysis with SMACC and PPI Algorithms for Endmember Extraction." In Communications in Computer and Information Science, 319–28. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9181-1_28.

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Synthiya Vinothini, D., B. Sathyabama, and S. Karthikeyan. "Super Resolution Mapping of Trees for Urban Forest Monitoring in Madurai City Using Remote Sensing." In Computer Vision, Graphics, and Image Processing, 88–96. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68124-5_8.

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Conference papers on the topic "Remote sensing Image processing Remote sensing Remote sensing Computer algorithms"

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"REMOTE SENSING CLASSIFICATION USING MULTI-SENSOR SUPER-RESOLUTION ALGORITHM." In 14th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing. IADIS Press, 2020. http://dx.doi.org/10.33965/cgv2020_202011l016.

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Васин, Дмитрий, and Dmitriy Vasin. "Regular methods for coding of raster images of remote sensing of Earth." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-1-152-158.

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Abstract:
The work is devoted to the consideration of the issues of eliminating information redundancy of raster data of remote sensing of the Earth (RDRSE), including the latest hyperspectral data (HSD). The characteristic properties of raster hyperspectral images (RHSI) are listed, a brief description of the existing methods of RDRSE compression is given. The possibility of using local, homogeneous "well-adapted" basic functions (LHWABF) to eliminate information redundancy and adaptive compression of RDRSE is considered. An algorithm for constructing a LHWABF system for the RHSI based on the Chebyshev approximation is proposed. The results of computational experiments are given. The effectiveness of the proposed method of adaptive compression RHSI is shown.
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Harding, Patrick J., Gordon Arthur, and Neil M. Robertson. "Metrics for measuring the impact of image processing algorithms on background statistics." In SPIE Remote Sensing, edited by Lorenzo Bruzzone, Claudia Notarnicola, and Francesco Posa. SPIE, 2008. http://dx.doi.org/10.1117/12.800308.

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Bi, Siwen, Hao Chen, Yuxian Ke, Siwei Rao, and Jiaying Liu. "Processing algorithms for quantum remote sensing image data." In Infrared Remote Sensing and Instrumentation XXVII, edited by Marija Strojnik and Gabriele E. Arnold. SPIE, 2019. http://dx.doi.org/10.1117/12.2528307.

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Fonseca, Leila Maria Gar, Laercio Massaru Namikawa, and Emiliano Ferreira Castejon. "Digital Image Processing in Remote Sensing." In 2009 Tutorials of the XXII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI). IEEE, 2009. http://dx.doi.org/10.1109/sibgrapi-tutorials.2009.13.

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Tusa, Laura, Mahdi Khodadadzadeh, I. Cecilia Contreras Acosta, and Richard Gloaguen. "Subspace clustering algorithms for mineral mapping." In Image and Signal Processing for Remote Sensing, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2018. http://dx.doi.org/10.1117/12.2500080.

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Pesántez-Cobos, Paúl, Francisco Alonso-Sarría, and Fulgencio Cánovas-García. "Implementing and validating of pan-sharpening algorithms in open-source software." In Image and Signal Processing for Remote Sensing, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2017. http://dx.doi.org/10.1117/12.2277543.

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Li, Feng, Lei Xin, Jie Fu, Puming Huang, and Yang Liu. "High efficient optical remote sensing images acquisition for nanosatellite reconstruction algorithms." In Image and Signal Processing for Remote Sensing, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2017. http://dx.doi.org/10.1117/12.2278180.

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Caves, R. G. "Multi-channel SAR segmentation: algorithms and applications." In IEE Colloquium on Image Processing for Remote Sensing. IEE, 1996. http://dx.doi.org/10.1049/ic:19960156.

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Plaza, Antonio, David Valencia, Javier Plaza, Juan Sanchez-Testal, Sergio Munoz, and Soraya Blazquez. "Parallel Implementation of Hyperspectral Image Processing Algorithms." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.242.

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