Hyperspectral compressive sensing

Jingyuan Lv; Yunsong Li; Bormin Huang; Chengke Wu

doi:10.1117/12.860247

24 August 2010 Hyperspectral compressive sensing

Jingyuan Lv, Yunsong Li, Bormin Huang, Chengke Wu

Proceedings Volume 7810, Satellite Data Compression, Communications, and Processing VI; 781003 (2010) https://doi.org/10.1117/12.860247
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

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.

Citation Download Citation

Jingyuan Lv, Yunsong Li, Bormin Huang, and Chengke Wu "Hyperspectral compressive sensing", Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 781003 (24 August 2010); https://doi.org/10.1117/12.860247

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