Low complexity regression wavelet analysis variants for hyperspectral data lossless compression

Sara Álvarez-Cortés, Naoufal Amrani, Joan Serra-Sagristà

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)


© 2017 Informa UK Limited, trading as Taylor & Francis Group. The evolution of the optical and of the sounding interferometer instruments along with the increase of the spaceborne storage capacity allows for the acquisition of large data volumes. However, the strongly limited downlink bandwidth unveils an insufficient on-board storage capacity, and the on-the-ground storage and dissemination are also contested. In these scenarios, data compression techniques are demanded. We discuss here the regression wavelet analysis (RWA) spectral transform, introducing novel variants that lead to an improved lossless coding performance. A comprehensive comparison with state-of-the-art remote-sensing data compression techniques shows the competitive behaviour of RWA in terms of lossless coding performance (yielding lower bit rates), computational complexity (requesting lower execution time), and other signal measurements (decreasing energy, mutual information, and entropy). Experimental results are performed on uncalibrated and calibrated data from Airborne Visible/Infrared Imaging Spectrometer, from Hyperion instrument and from Infrared Atmospheric Sounding Interferometer.
Original languageEnglish
Pages (from-to)1971-2000
JournalInternational Journal of Remote Sensing
Issue number7
Publication statusPublished - 3 Apr 2018


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