Compression of hyperspectral scenes through integer-to-integer spectral graph transforms+

Dion Eustathios Olivier Tzamarias*, Kevin Chow, Ian Blanes, Joan Serra-Sagristà

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Hyperspectral images are depictions of scenes represented across many bands of the electromagnetic spectrum. The large size of these images as well as their unique structure requires the need for specialized data compression algorithms. The redundancies found between consecutive spectral components and within components themselves favor algorithms that exploit their particular structure. One novel technique with applications to hyperspectral compression is the use of spectral graph filterbanks such as the GraphBior transform, that leads to competitive results. Such existing graph based filterbank transforms do not yield integer coefficients, making them appropriate only for lossy image compression schemes. We propose here two integer-to-integer transforms that are used in the biorthogonal graph filterbanks for the purpose of the lossless compression of hyperspectral scenes. Firstly, by applying a Triangular Elementary Rectangular Matrix decomposition on GraphBior filters and secondly by adding rounding operations to the spectral graph lifting filters. We examine the merit of our contribution by testing its performance as a spatial transform on a corpus of hyperspectral images; and share our findings through a report and analysis of our results.

Original languageAmerican English
Article number2290
JournalRemote Sensing
Volume11
Issue number19
DOIs
Publication statusPublished - 1 Oct 2019

Keywords

  • Graph filterbanks
  • Graph signal processing
  • Hyperspectral image coding
  • Integer-to-integer transforms

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