TY - BOOK ID - 145308458 TI - Remote Sensing Data Compression AU - Lukin, Vladimir AU - Vozel, Benoit AU - Serra-Sagristà , Joan PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Technology: general issues KW - on-board data compression KW - CCSDS 123.0-B-2 KW - near-lossless hyperspectral image compression KW - hyperspectral image coding KW - graph filterbanks KW - integer-to-integer transforms KW - graph signal processing KW - compact data structure KW - quadtree KW - k2-tree KW - k2-raster KW - DACs KW - 3D-CALIC KW - M-CALIC KW - hyperspectral images KW - fully convolutional network KW - semantic segmentation KW - spectral image KW - tensor decomposition KW - HEVC KW - intra coding KW - JPEG 2000 KW - high bit-depth compression KW - multispectral satellite images KW - crop classification KW - Landsat-8 KW - Sentinel-2 KW - Elias codes KW - Simple9 KW - Simple16 KW - PForDelta KW - Rice codes KW - hyperspectral scenes KW - hyperspectral image KW - lossy compression KW - real time KW - FPGA KW - PCA KW - JPEG2000 KW - EBCOT KW - multispectral KW - hyperspectral KW - CCSDS KW - FAPEC KW - data compression KW - transform KW - hyperspectral imaging KW - on-board processing KW - GPU KW - real-time performance KW - UAV KW - parallel computing KW - remote sensing KW - image quality KW - image classification KW - visual quality metrics KW - spectral–spatial feature KW - multispectral image compression KW - partitioned extraction KW - group convolution KW - rate-distortion KW - compressed sensing KW - invertible projection KW - coupled dictionary KW - singular value KW - task-driven learning KW - on board compression KW - transform coding KW - learned compression KW - neural networks KW - variational autoencoder KW - complexity KW - real-time compression KW - on-board compression KW - real-time transmission KW - UAVs KW - compressive sensing KW - synthetic aperture sonar KW - underwater sonar imaging KW - remote sensing data compression KW - lossless compression KW - compression impact KW - computational complexity KW - on-board data compression KW - CCSDS 123.0-B-2 KW - near-lossless hyperspectral image compression KW - hyperspectral image coding KW - graph filterbanks KW - integer-to-integer transforms KW - graph signal processing KW - compact data structure KW - quadtree KW - k2-tree KW - k2-raster KW - DACs KW - 3D-CALIC KW - M-CALIC KW - hyperspectral images KW - fully convolutional network KW - semantic segmentation KW - spectral image KW - tensor decomposition KW - HEVC KW - intra coding KW - JPEG 2000 KW - high bit-depth compression KW - multispectral satellite images KW - crop classification KW - Landsat-8 KW - Sentinel-2 KW - Elias codes KW - Simple9 KW - Simple16 KW - PForDelta KW - Rice codes KW - hyperspectral scenes KW - hyperspectral image KW - lossy compression KW - real time KW - FPGA KW - PCA KW - JPEG2000 KW - EBCOT KW - multispectral KW - hyperspectral KW - CCSDS KW - FAPEC KW - data compression KW - transform KW - hyperspectral imaging KW - on-board processing KW - GPU KW - real-time performance KW - UAV KW - parallel computing KW - remote sensing KW - image quality KW - image classification KW - visual quality metrics KW - spectral–spatial feature KW - multispectral image compression KW - partitioned extraction KW - group convolution KW - rate-distortion KW - compressed sensing KW - invertible projection KW - coupled dictionary KW - singular value KW - task-driven learning KW - on board compression KW - transform coding KW - learned compression KW - neural networks KW - variational autoencoder KW - complexity KW - real-time compression KW - on-board compression KW - real-time transmission KW - UAVs KW - compressive sensing KW - synthetic aperture sonar KW - underwater sonar imaging KW - remote sensing data compression KW - lossless compression KW - compression impact KW - computational complexity UR - https://www.unicat.be/uniCat?func=search&query=sysid:145308458 AB - A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interesting ER -