Editorial to Special Issue “Remote Sensing Data Compression”

Benoit Vozel; Vladimir Lukin; Joan Serra-Sagristà

doi:10.3390/rs13183727

Remote Sensing (Sep 2021)

Editorial to Special Issue “Remote Sensing Data Compression”

Benoit Vozel,
Vladimir Lukin,
Joan Serra-Sagristà

Affiliations

Benoit Vozel: Engineering School of Applied Sciences and Technology, University of Rennes 1, 22305 Lannion, France
Vladimir Lukin: Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkov, Ukraine
Joan Serra-Sagristà: Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, Cerdanyola del Valles, 08290 Barcelona, Catalonia, Spain

DOI: https://doi.org/10.3390/rs13183727
Journal volume & issue: Vol. 13, no. 18
p. 3727

Abstract

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A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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Abstract

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