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2020-09-11

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cc-by (c) Hernández Cabronero, Miguel et al., 2020
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/174903

High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation

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https://doi.org/10.3390/rs12182955

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.

Subject

Imatges hiperespectrals, Algorismes

Subject (English)

Hyperspectral imaging, Algorithms

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Articles publicats en revistes (Institut de Ciències del Cosmos (ICCUB))

Citation

HERNÁNDEZ CABRONERO, Miguel, et al. High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation. Remote Sensing. 2020. Vol. 12, num. 18. ISSN 2072-4292. [consulted: 19 of August of 2026]. Available at: https://hdl.handle.net/2445/174903

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