Remote Sensing Data Compression
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 o...
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| Format: | Online |
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| Jezik: | engleski |
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MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Teme: | |
| Online pristup: | ONIX_20220111_9783036523033_874 |
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| _version_ | 1869520649694740480 |
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| collection | Directory of Open Access Books |
| description | 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 |
| format | Online |
| id | doab-20.500.12854ir-77042 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-770422024-04-09T23:16:39Z Remote Sensing Data Compression Lukin, Vladimir Vozel, Benoit Serra-Sagristà, Joan on-board data compression CCSDS 123.0-B-2 near-lossless hyperspectral image compression hyperspectral image coding graph filterbanks integer-to-integer transforms graph signal processing compact data structure quadtree k2-tree k2-raster DACs 3D-CALIC M-CALIC hyperspectral images fully convolutional network semantic segmentation spectral image tensor decomposition HEVC intra coding JPEG 2000 high bit-depth compression multispectral satellite images crop classification Landsat-8 Sentinel-2 Elias codes Simple9 Simple16 PForDelta Rice codes hyperspectral scenes hyperspectral image lossy compression real time FPGA PCA JPEG2000 EBCOT multispectral hyperspectral CCSDS FAPEC data compression transform hyperspectral imaging on-board processing GPU real-time performance UAV parallel computing remote sensing image quality image classification visual quality metrics spectral–spatial feature multispectral image compression partitioned extraction group convolution rate-distortion compressed sensing invertible projection coupled dictionary singular value task-driven learning on board compression transform coding learned compression neural networks variational autoencoder complexity real-time compression on-board compression real-time transmission UAVs compressive sensing synthetic aperture sonar underwater sonar imaging remote sensing data compression lossless compression compression impact computational complexity thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues 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 2022-01-11T13:50:14Z 2022-01-11T13:50:14Z 2021 book ONIX_20220111_9783036523033_874 9783036523033 9783036523040 https://directory.doabooks.org/handle/20.500.12854/77042 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4652 https://mdpi.com/books/pdfview/book/4652 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-2304-0 10.3390/books978-3-0365-2304-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036523033 9783036523040 366 Basel, Switzerland open access |
| spellingShingle | on-board data compression CCSDS 123.0-B-2 near-lossless hyperspectral image compression hyperspectral image coding graph filterbanks integer-to-integer transforms graph signal processing compact data structure quadtree k2-tree k2-raster DACs 3D-CALIC M-CALIC hyperspectral images fully convolutional network semantic segmentation spectral image tensor decomposition HEVC intra coding JPEG 2000 high bit-depth compression multispectral satellite images crop classification Landsat-8 Sentinel-2 Elias codes Simple9 Simple16 PForDelta Rice codes hyperspectral scenes hyperspectral image lossy compression real time FPGA PCA JPEG2000 EBCOT multispectral hyperspectral CCSDS FAPEC data compression transform hyperspectral imaging on-board processing GPU real-time performance UAV parallel computing remote sensing image quality image classification visual quality metrics spectral–spatial feature multispectral image compression partitioned extraction group convolution rate-distortion compressed sensing invertible projection coupled dictionary singular value task-driven learning on board compression transform coding learned compression neural networks variational autoencoder complexity real-time compression on-board compression real-time transmission UAVs compressive sensing synthetic aperture sonar underwater sonar imaging remote sensing data compression lossless compression compression impact computational complexity thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Remote Sensing Data Compression |
| title | Remote Sensing Data Compression |
| title_full | Remote Sensing Data Compression |
| title_fullStr | Remote Sensing Data Compression |
| title_full_unstemmed | Remote Sensing Data Compression |
| title_short | Remote Sensing Data Compression |
| title_sort | remote sensing data compression |
| topic | on-board data compression CCSDS 123.0-B-2 near-lossless hyperspectral image compression hyperspectral image coding graph filterbanks integer-to-integer transforms graph signal processing compact data structure quadtree k2-tree k2-raster DACs 3D-CALIC M-CALIC hyperspectral images fully convolutional network semantic segmentation spectral image tensor decomposition HEVC intra coding JPEG 2000 high bit-depth compression multispectral satellite images crop classification Landsat-8 Sentinel-2 Elias codes Simple9 Simple16 PForDelta Rice codes hyperspectral scenes hyperspectral image lossy compression real time FPGA PCA JPEG2000 EBCOT multispectral hyperspectral CCSDS FAPEC data compression transform hyperspectral imaging on-board processing GPU real-time performance UAV parallel computing remote sensing image quality image classification visual quality metrics spectral–spatial feature multispectral image compression partitioned extraction group convolution rate-distortion compressed sensing invertible projection coupled dictionary singular value task-driven learning on board compression transform coding learned compression neural networks variational autoencoder complexity real-time compression on-board compression real-time transmission UAVs compressive sensing synthetic aperture sonar underwater sonar imaging remote sensing data compression lossless compression compression impact computational complexity thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | on-board data compression CCSDS 123.0-B-2 near-lossless hyperspectral image compression hyperspectral image coding graph filterbanks integer-to-integer transforms graph signal processing compact data structure quadtree k2-tree k2-raster DACs 3D-CALIC M-CALIC hyperspectral images fully convolutional network semantic segmentation spectral image tensor decomposition HEVC intra coding JPEG 2000 high bit-depth compression multispectral satellite images crop classification Landsat-8 Sentinel-2 Elias codes Simple9 Simple16 PForDelta Rice codes hyperspectral scenes hyperspectral image lossy compression real time FPGA PCA JPEG2000 EBCOT multispectral hyperspectral CCSDS FAPEC data compression transform hyperspectral imaging on-board processing GPU real-time performance UAV parallel computing remote sensing image quality image classification visual quality metrics spectral–spatial feature multispectral image compression partitioned extraction group convolution rate-distortion compressed sensing invertible projection coupled dictionary singular value task-driven learning on board compression transform coding learned compression neural networks variational autoencoder complexity real-time compression on-board compression real-time transmission UAVs compressive sensing synthetic aperture sonar underwater sonar imaging remote sensing data compression lossless compression compression impact computational complexity thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20220111_9783036523033_874 |