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Title
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Compression of hyperspectral images using compressed sensing and block coordinate descent search
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Author
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Abstract
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| In this paper, a new lossy compression method for hyperspectral images (HSI) is introduced based on the Non-Negative Tucker Decomposition (NTD). HSI are considered as a 3D dataset: two spatial dimensions and one spectral dimension. The NTD algorithm decomposes the original data into a smaller 3D dataset (core tensor) and three matrices. In the proposed method, in order to find the optimal decomposition, the Block Coordinate Descent (BCD) method is used, which is initialised by using Compressed Sensing (CS). The obtained optimal core tensor and matrices are coded by applying arithmetic coding and finally the compressed dataset is transmitted. The proposed method is applied to real datasets. Our experimental results show that, in comparison with stateof- the-art lossy compression methods, the proposed method achieves the highest signal to noise ratio (SNR) at any desired compression ratio (CR) while noise reduction is simultaneously obtained. |
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Language
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English
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Source (book)
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IEEE Whispers 2016 : 8th Workshop on Hyperspectral Image and Signal Processing: evolution in remote sensing, 21-24 August 2016, Los Angeles
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Publication
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S.l.
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IEEE
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2016
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ISBN
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978-1-5090-0608-3
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Volume/pages
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p. 1-4
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ISI
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000425944200128
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