Carregant...
Fitxers
Tipus de document
ArticleVersió
Versió publicadaData de publicació
Llicència de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/174903
High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation
Títol de la revista
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
Resum
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.
Matèries
Matèries (anglès)
Citació
Citació
HERNÁNDEZ CABRONERO, Miguel, PORTELL I DE MORA, Jordi, BLANES, Ian, SERRA SAGRISTÀ, Joan. High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation. _Remote Sensing_. 2020. Vol. 12, núm. 18. [consulta: 24 de gener de 2026]. ISSN: 2072-4292. [Disponible a: https://hdl.handle.net/2445/174903]