Remote Sensing Data Compression

• Autore: Lukin Vladimir
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 electronic resource (366 p.)
• Soggetto topico: Technology: general issues
• Soggetto non controllato: 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
• Persona (resp. second.): VozelBenoit; Serra-SagristàJoan; LukinVladimir
• Sommario/riassunto: 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
• Titolo autorizzato: Remote Sensing Data Compression
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557383103321
• Lo trovi qui: Univ. Federico II