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Proceedings Paper

Compression of hyperspectral imagery based on compressive sensing and interband prediction
Author(s): Haiying Liu; Yunsong Li; Chengke Wu; Keyan Wang; Yu Wang
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Paper Abstract

An efficient compression algorithm for hyperspectral imagery based on compressive sensing and interband linear prediction is proposed which has the advantages of high compression performance and low computational complexity by exploiting the strong spectral correlation. At the encoder, the random measurements of each frame are made, quantized and transmitted to the decoder independently. The prediction parameters between adjacent bands are also estimated using the linear prediction algorithm and transmitted to the decoder. At the decoder, a new reconstruction algorithm with the proposed initialization and stopping criterion is employed to reconstruct the current frames with the assistance of the prediction frame, which is derived from the previous reconstructed neighboring frames and the received prediction parameters using the same prediction algorithm. Experimental results show that the proposed algorithm not only obtains about 1.1 dB gains but greatly decreases decoding complexity. Furthermore, our algorithm has the characteristics of low-complexity encoding and facility in hardware implementation.

Paper Details

Date Published: 24 August 2010
PDF: 9 pages
Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 781016 (24 August 2010); doi: 10.1117/12.859658
Show Author Affiliations
Haiying Liu, Xidian Univ. (China)
Yunsong Li, Xidian Univ. (China)
Chengke Wu, Xidian Univ. (China)
Keyan Wang, Xidian Univ. (China)
Yu Wang, Xi'an Research Institute of Surveying and Mapping (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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