Please use this identifier to cite or link to this item:
Title: High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation
Author: Hernández Cabronero, Miguel
Portell i de Mora, Jordi
Blanes, Ian
Serra Sagristà, Joan
Keywords: Imatges hiperespectrals
Hyperspectral imaging
Issue Date: 11-Sep-2020
Publisher: MDPI
Abstract: The capacity of the downlink channel is a major bottleneck for applications based on remotesensing hyperspectral imagery (HSI). Data compression is an essential tool to maximize the amountof HSI scenes that can be retrieved on the ground. At the same time, energy and hardware constraintsof spaceborne devices impose limitations on the complexity of practical compression algorithms.To avoid any distortion in the analysis of the HSI data, only lossless compression is considered in thisstudy. This work aims at finding the most advantageous compression-complexity trade-off withinthe state of the art in HSI compression. To do so, a novel comparison of the most competitive spectraldecorrelation approaches combined with the best performing low-complexity compressors of thestate is presented. Compression performance and execution time results are obtained for a set of47 HSI scenes produced by 14 different sensors in real remote sensing missions. Assuming onlya limited amount of energy is available, obtained data suggest that the FAPEC algorithm yields thebest trade-off. When compared to the CCSDS 123.0-B-2 standard, FAPEC is 5.0 times faster andits compressed data rates are on average within 16% of the CCSDS standard. In scenarios whereenergy constraints can be relaxed, CCSDS 123.0-B-2 yields the best average compression results of allevaluated methods.
Note: Reproducció del document publicat a:
It is part of: Remote Sensing, 2020, vol. 12, num. 18
Related resource:
ISSN: 2072-4292
Appears in Collections:Articles publicats en revistes (Institut de Ciències del Cosmos (ICCUB))

Files in This Item:
File Description SizeFormat 
703648.pdf929.42 kBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons