Relevant bibliographies by topics / Image processing Remote sensing

Contents

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

Academic literature on the topic 'Image processing Remote sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Image processing Remote sensing"

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Camps-Valls, Gustavo, Devis Tuia, Luis Gómez-Chova, Sandra Jiménez, and Jesús Malo. "Remote Sensing Image Processing." Synthesis Lectures on Image, Video, and Multimedia Processing 5, no. 1 (2011): 1–192. http://dx.doi.org/10.2200/s00392ed1v01y201107ivm012.

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Wang, Yi Ding, and Shuai Qin. "Remote Sensing Image Mosaic Algorithm." Key Engineering Materials 500 (January 2012): 716–21. http://dx.doi.org/10.4028/www.scientific.net/kem.500.716.

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In the field of remote sensing, the acquirement of higher resolution of remote sensing images has become a hot spot issue with widely use of high resolution of remote sensing images. This paper focus on the characteristics of high resolution remote sensing images, on the basis of fully considerate of the correlation between geometric features and image pixels, bring forward a fusion of image mosaic processing algorithm. With this algorithm, the surface features can be well preserved after the processing of mosaic the remote sensing images, and the overlapping area can transit naturally, it will be better for the post-processing, analysis and application.
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Zhu, Zhiqin, Yaqin Luo, Hongyan Wei, et al. "Atmospheric Light Estimation Based Remote Sensing Image Dehazing." Remote Sensing 13, no. 13 (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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Jiang, W., S. Chen, X. Wang, Q. Huang, H. Shi, and Y. Man. "REMOTE SENSING IMAGE QUALITY ASSESSMENT EXPERIMENT WITH POST-PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 665–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-665-2018.

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This paper briefly describes the post-processing influence assessment experiment, the experiment includes three steps: the physical simulation, image processing, and image quality assessment. The physical simulation models sampled imaging system in laboratory, the imaging system parameters are tested, the digital image serving as image processing input are produced by this imaging system with the same imaging system parameters. The gathered optical sampled images with the tested imaging parameters are processed by 3 digital image processes, including calibration pre-processing, lossy compression with different compression ratio and image post-processing with different core. Image quality assessment method used is just noticeable difference (JND) subject assessment based on ISO20462, through subject assessment of the gathered and processing images, the influence of different imaging parameters and post-processing to image quality can be found. The six JND subject assessment experimental data can be validated each other. Main conclusions include: image post-processing can improve image quality; image post-processing can improve image quality even with lossy compression, image quality with higher compression ratio improves less than lower ratio; with our image post-processing method, image quality is better, when camera MTF being within a small range.
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Zhao, Shang Min, Wei Ming Cheng, and Xi Chen. "Exploration of Remote Sensing Image Processing Method for Glacial Geomorphology Research." Key Engineering Materials 500 (January 2012): 437–43. http://dx.doi.org/10.4028/www.scientific.net/kem.500.437.

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Taking Mt. Namjagbarwa region as an example, this paper explores a complete remote sensing image processing method for glacial geomorphology research. Based on the selection of Landsat7 ETM+ images, the remote sensing image processing method such as band selection, overlap, fusion, mosaic and so on is carried out. The result shows: ① right selection of remote sensing images and proper process based on the characteristics of research area and research purpose, not only reduce the process difficulty, but make a firm foundation for subsequent glacial geomorphology research; ②according to the computation of correlation coefficient between fusion images and original multi-spectral images, panchromatic high resolution images, the result shows that the principle component transformation method has better effect than IHS transformation method in remote sensing image fusion process.
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Li, J., J. Sheng, Y. Chen, et al. "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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Fu, N., L. Sun, H. Z. Yang, J. Ma, and B. Q. Liao. "RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 565–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-565-2020.

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Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.
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Li, Su, Wen Chao Wang, Lu Wen Li, and Jian Jun Zhou. "Survey of Support Vector Machine in the Processing of Remote Sensing Image." Advanced Materials Research 774-776 (September 2013): 1567–72. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1567.

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Support vector machine is a kind of machine learning algorithm which is based on statistical learning theory and VC dimension theory and structural risk minimization principle, it can solve data classification and regression problems. With the in-depth research of support vector machine, in the field of remote sensing image processing applications are also obtained the very big development. This paper first gives a brief introduction of the theory of support vector machine, and then summarized the progress in remote sensing image compression, geometric correction of remote sensing images, processing of remote sensing image data classification research, finally proposed the trend of the support vector machine in application and development in the field of remote sensing image processing problems.
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Shu, Chang, and Lihui Sun. "Automatic target recognition method for multitemporal remote sensing image." Open Physics 18, no. 1 (2020): 170–81. http://dx.doi.org/10.1515/phys-2020-0015.

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AbstractThe traditional target recognition method for the remote sensing image is difficult to accurately identify the specified targets from the massive remote sensing image data. Based on the theory of multitemporal recognition, an automatic target recognition method for the remote sensing image is proposed in this article. The proposed recognition method includes four modules: automatic segmentation of multitemporal remote sensing image, automatic target extraction of multitemporal remote sensing image, automatic processing of multitemporal remote sensing image, and automatic recognition of multitemporal remote sensing image. The automatic segmentation of the image target is introduced. The effectiveness of the segmentation technology is verified through the kernel function bandwidth algorithm. Linear feature extraction is used to extract the segmented image. The image extraction processing is described, which includes image profile analysis, image preprocessing, image feature analysis, the region of interest localization, image enhancement processing, recognition processing, and result output. According to the theory of pattern recognition, three different feature recognition images are given, which are partial separable recognition, weakly separable recognition, and fully separable recognition, and then, a new image recognition method is designed. To verify the practical application effect of the recognition method, the proposed method is compared with the traditional recognition method. Experimental results show that the proposed method can accurately identify the specified objects from the massive remote sensing image data and has a high potential for development. This article has an important guiding significance for image recognition.
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Fan, Peili. "Combined with Local Neighborhood Characteristics and Remote Sensing Image Fusion Method of C-BEMD." International Journal of Circuits, Systems and Signal Processing 15 (August 12, 2021): 936–44. http://dx.doi.org/10.46300/9106.2021.15.100.

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For the sake of ameliorate the high resolution recognition capacity building remote sensing images, a remote sensing image fusion method based on local neighborhood characteristics and C-BEMD is advanced. The building remote sensing image acquisition model and the building remote sensing image picture element edge feature detection model are designed. The wavelet multi-scale denoising method is used to suppress the fuzzy spread of picture element feature points between image residual units, extract the geometric feature points of image sequence, and process the building remote sensing image block by block. The global residual learning and message fusion of building remote sensing image are implemented. The local neighborhood feature matching method is used to reconstruct the building remote sensing image region. Combined with the C-BEMD empirical mode decomposition method, the building remote sensing image fusion and feature point matching in affine region are implemented, and the block image template matching method is used to realize the automatic fusion and recognition of building remote sensing image. Simulation results show that this method has high precision in constructing remote sensing image fusion and good positioning performance in constructing remote sensing image feature points.
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Dissertations / Theses on the topic "Image processing Remote sensing"

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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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Bosdogianni, Panagiota. "Mixed pixel classification in remote sensing." Thesis, University of Surrey, 1996. http://epubs.surrey.ac.uk/843999/.

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This thesis is concerned with the problem of mixed pixel classification in Remote sensing applications and attempts to find accurate and robust solutions to this problem. The application we are interested in, is to monitor burned forest regions for a few years after the fire in order to identify the type of vegetation present in these areas and consequently assess the danger of desertification. The areas of interest are semi-arid where the vegetation tends to vary at smaller scales than the area covered by a single Landsat TM pixel, thus mixed pixels are quite common. In this thesis we considered whole sets of mixed pixels. First, an overview of the methods currently used to solve the mixed pixel classification problem is presented, focused on the linear mixing model which is adopted in this thesis. Then a method that incorporates higher order moments of the distributions of the pure and the mixed classes is proposed. This method is shown to augment the number of equations used for the classification and theoretically it allows the specification of more cover classes than there are bands available, without compromising the accuracy of the results. The problem of deterioration of the classification performance, due to inaccuracies in calculation of the statistics when outliers are present, is also examined. The use of the Hough Transform is proposed for the linear unmixing in order to provide robust estimates even in cases where outliers are present. The Hough transform method though, is an exhaustive method and therefore has higher computational complexity. Furthermore, its performance, in the absence of outliers, is not as good as the solution obtained by the Least Squares Error method. Hence, the Randomized Hough Transform is proposed in order to improve the computational speed and maintain the same level of performance, while the Hypothesis Testing Hough Transform is proposed to improve the accuracy of the classification results. All the methods proposed in this thesis have been compared with the Least Squares Error method using simulated and real Landsat TM image data, in order to illustrate the validity and usefulness of the proposed algorithms.
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Bishoff, Josef P. "Target detection using oblique hyperspectral imagery : a domain trade study /." Online version of thesis, 2008. http://hdl.handle.net/1850/7834.

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Munechika, Curtis K. "Merging panchromatic and multispectral images for enhanced image analysis /." Online version of thesis, 1990. http://hdl.handle.net/1850/11366.

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Glotfelty, Joseph Edmund. "Automatic selection of optimal window size and shape for texture analysis." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=898.

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Thesis (M.A.)--West Virginia University, 1999.<br>Title from document title page. Document formatted into pages; contains vii, 59 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 55-59).
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Stefanou, Marcus S. "Spectral image utility for target detection applications /." Online version of thesis, 2008. http://hdl.handle.net/1850/7043.

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Vyas, Sarweshwar Prasad. "Radar remote sensing for monitoring sugar beet production." Thesis, University of Nottingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363556.

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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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Wanderley, Juliana Fernandes Camapum. "Colour-based recognition for remote sensing in environmental systems." Thesis, Coventry University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266844.

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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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Books on the topic "Image processing Remote sensing"

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Girard, Michel-Claude. Processing of remote sensing data. Balkema, 2001.

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Processing of remote sensing data. Balkema, 2003.

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Hyperspectral remote sensing. SPIE, 2012.

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A, Schowengerdt Robert, ed. Remote sensing, models, and methods for image processing. 2nd ed. Academic Press, 1997.

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Signal and image processing for remote sensing. CRC/Taylor & Francis, 2007.

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Liu, Jian Guo, and Philippa J. Mason. Image Processing and GIS for Remote Sensing. John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118724194.

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Moser, Gabriele, and Josiane Zerubia, eds. Mathematical Models for Remote Sensing Image Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-66330-2.

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Image registration for remote sensing. Cambridge University Press, 2011.

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Introductory digital image processing: A remote sensing perspective. Prentice-Hall, 1986.

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R, Jensen John. Introductory digital image processing: A remote sensing perspective. 2nd ed. Prentice Hall, 1996.

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Book chapters on the topic "Image processing Remote sensing"

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Gupta, Ravi Prakash. "Digital Image Processing." In Remote Sensing Geology. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-12914-2_12.

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Dwivedi, Ravi Shankar. "Digital Image Processing." In Remote Sensing of Soils. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-53740-4_3.

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Peddle, Derek R., Philippe M. Teillet, and Michael A. Wulder. "Radiometric Image Processing." In Remote Sensing of Forest Environments. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0306-4_7.

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Gupta, Ravi Prakash. "Digital Image Processing of Multispectral Data." In Remote Sensing Geology. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05283-9_10.

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Gupta, Ravi P. "Digital Image Processing of Multispectral Data." In Remote Sensing Geology. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-55876-8_13.

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Tupin, Florence, Jordi Inglada, and Grégoire Mercier. "Image Processing Techniques for Remote Sensing." In Remote Sensing Imagery. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118899106.ch5.

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Goodman, Dean, and Salvatore Piro. "GPR Image Construction and Image Processing." In GPR Remote Sensing in Archaeology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31857-3_4.

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Wang, Liguo, and Chunhui Zhao. "Introduction of Hyperspectral Remote Sensing Applications." In Hyperspectral Image Processing. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47456-3_9.

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Zhang, Chen, and Li Lin. "Image Processing Methods in Agricultural Observation Systems." In Springer Remote Sensing/Photogrammetry. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66387-2_6.

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

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Conference papers on the topic "Image processing Remote sensing"

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Huot, Etienne, Jean-Paul Rudant, Nicholas Classeau, et al. "Image processing for multitemporal SAR images." In Remote Sensing, edited by Francesco Posa. SPIE, 1998. http://dx.doi.org/10.1117/12.331352.

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Yamamoto, Hiromichi, Kohzo Homma, Toshio Isobe, Masao Naka, Satsuki Matsumura, and Hideo Tameishi. "Remotely sensed image processing with multistage inferences." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1999. http://dx.doi.org/10.1117/12.373237.

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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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Liu, Zhaohua, and Jingyu Yang. "Remote sensing image parallel processing system." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jianguo Liu, Kunio Doi, Aaron Fenster, and S. C. Chan. SPIE, 2009. http://dx.doi.org/10.1117/12.832392.

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Kalawsky, Roy S. "Polarimetric image processing for remote sensing." In Orlando '90, 16-20 April, edited by James A. Smith. SPIE, 1990. http://dx.doi.org/10.1117/12.21409.

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Lopez-Ornelas, Erick, Florence Laporterie-Dejean, and Guy Flouzat. "Satellite image segmentation using graph representation and morphological processing." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2004. http://dx.doi.org/10.1117/12.511221.

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Li, Deren, and Liangpei Zhang. "Processing of hyperspectral remote sensing images." In International Symposium on Multispectral Image Processing, edited by Ji Zhou, Anil K. Jain, Tianxu Zhang, Yaoting Zhu, Mingyue Ding, and Jianguo Liu. SPIE, 1998. http://dx.doi.org/10.1117/12.323645.

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Rosin, Paul L. "Refining region estimates for post-processing image classification." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196718.

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Tong, Qingxi, Lanfen Zheng, Yongqi Xue, Bing Zhang, Yongchao Zhao, and Liangyun Liu. "Hyperspectral remote sensing in China." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441358.

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Vidal-Pantaleoni, Ana, and Miguel Ferrando. "Comparison of SAR processing SPECAN techniques for efficient ScanSAR image generation." In Remote Sensing, edited by Francesco Posa. SPIE, 1998. http://dx.doi.org/10.1117/12.331360.

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Reports on the topic "Image processing Remote sensing"

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Iisaka, J., and T. Sakurai-Amano. PC Network of Image Computing for Remote Sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/218514.

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Lal, Anisha M., Ali A. Abdulla, and Aju Dennisan. Remote Sensing Image Restoration for Environmental Applications Using Estimated Parameters. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.08.11.

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Ducey, Craig. Hierarchical Image Analysis and Characterization of Scaling Effects in Remote Sensing. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.399.

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McNairn, H., J. C. Deguise, J. Secker, and J. Shang. Development of Remote Sensing Image Products for Use in Precision Farming. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219750.

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Yu, Bin. Statistical Problems in Remote Sensing, Image Compression, and Mapping of Human Chromosomes. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada413806.

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Collins, Leslie M., Peter A. Torrione, and Kenneth D. Morton. Statistical Signal Processing for Remote Sensing of Targets: Proposal for Terrestrial Science Program. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada614713.

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Khan, Shuhab D. Mapping Alteration Caused by Hydrocarbon Microseepages in Patrick Draw area Southwest Wyoming Using Image Spectroscopy and Hyperspectral Remote Sensing. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/918416.

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Peterson, Erin D., Scott D. Brown, Timothy J. Hattenberger, and John R. Schott. Surface and Buried Landmine Scene Generation and Validation Using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada424769.

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Smyre, J. L., M. E. Hodgson, B. W. Moll, A. L. King, and Yang Cheng. Daytime multispectral scanner aerial surveys of the Oak Ridge Reservation, 1992--1994: Overview of data processing and analysis by the Environmental Restoration Remote Sensing Program, Fiscal year 1995. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/204019.

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Smyre, J. L., B. W. Moll, and A. L. King. Gamma radiological surveys of the Oak Ridge Reservation, Paducah Gaseous Diffusion Plant, and Portsmouth Gaseous Diffusion Plant, 1990-1993, and overview of data processing and analysis by the Environmental Restoration Remote Sensing Program, Fiscal Year 1995. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/262973.

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