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Proceedings Paper

Hyperspectral compressive sensing
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Paper Abstract

Compressive sensing (CS) is a new technique for reconstructing essentially sparse signals from a number of measurements smaller than the Nyquist-Shannon criterion. The application of CS to hyperspectral imaging has the potential for significantly reducing the sampling rate and hence the cost of the analog-to-digital sensors. In this paper a novel approach for hyperspectral compressive sensing is proposed where each band of hyperspectral imagery is sampled under the same measurement matrix. It is shown that the correlation between two neighboring band compressive sample values is consistent with that between two neighboring band pixel values. Our hyperspectral compressive sensing experimental results show that the proposed joint reconstruction method yields smaller reconstruction errors than the individual reconstruction method at various sampling rates.

Paper Details

Date Published: 24 August 2010
PDF: 12 pages
Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 781003 (24 August 2010); doi: 10.1117/12.860247
Show Author Affiliations
Jingyuan Lv, Xidian Univ. (China)
Yunsong Li, Xidian Univ. (China)
Bormin Huang, Univ. of Wisconsin-Madison (United States)
Chengke Wu, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 7810:
Satellite Data Compression, Communications, and Processing VI
Bormin Huang; Antonio J. Plaza; Joan Serra-Sagristà; Chulhee Lee; Yunsong Li; Shen-En Qian, Editor(s)

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