Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms
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...
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doaj-332286ee0e3841e8985cacf545dffad42020-11-25T01:15:08ZengMDPI AGRemote Sensing2072-42922019-09-011119229010.3390/rs11192290rs11192290Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph TransformsDion Eustathios Olivier Tzamarias0Kevin Chow1Ian Blanes2Joan Serra-Sagristà3Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, SpainDepartment of Information and Communications Engineering, Universitat Autònoma de Barcelona, Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, SpainDepartment of Information and Communications Engineering, Universitat Autònoma de Barcelona, Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, SpainDepartment of Information and Communications Engineering, Universitat Autònoma de Barcelona, Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, SpainHyperspectral 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.https://www.mdpi.com/2072-4292/11/19/2290hyperspectral image codinggraph filterbanksinteger-to-integer transformsgraph signal processing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dion Eustathios Olivier Tzamarias Kevin Chow Ian Blanes Joan Serra-Sagristà |
spellingShingle |
Dion Eustathios Olivier Tzamarias Kevin Chow Ian Blanes Joan Serra-Sagristà Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms Remote Sensing hyperspectral image coding graph filterbanks integer-to-integer transforms graph signal processing |
author_facet |
Dion Eustathios Olivier Tzamarias Kevin Chow Ian Blanes Joan Serra-Sagristà |
author_sort |
Dion Eustathios Olivier Tzamarias |
title |
Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms |
title_short |
Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms |
title_full |
Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms |
title_fullStr |
Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms |
title_full_unstemmed |
Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms |
title_sort |
compression of hyperspectral scenes through integer-to-integer spectral graph transforms |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-09-01 |
description |
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. |
topic |
hyperspectral image coding graph filterbanks integer-to-integer transforms graph signal processing |
url |
https://www.mdpi.com/2072-4292/11/19/2290 |
work_keys_str_mv |
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