Hyperspectral image reconstruction based on an improved genetic algorithm

Lang Wang; Shuxu Guo; Ruizhi Ren

doi:10.1117/12.825603

31 August 2009 Hyperspectral image reconstruction based on an improved genetic algorithm

Lang Wang, Shuxu Guo, Ruizhi Ren

Proceedings Volume 7455, Satellite Data Compression, Communication, and Processing V; 74550K (2009) https://doi.org/10.1117/12.825603
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

Abstract

A novel theory of information acquisition-"compressive sampling" has been applied in this paper, and goes against the common wisdom in data acquisition of Shannon theorem. CS theory asserts that one can recover certain signals and images perfectly from far fewer samples or measurements than traditional methods use. This paper presents an improvement on genetic algorithm instead of match pursuit algorithm in consideration of the enormous computational complexity on sparse decomposition. Then the whole image is divided into small blocks which can be processed by sparse decomposition, and an end to decomposition is determined by PSNR threshold adaptively. At last, the experiment results show that good performance on image reconstruction with less computational complexity has been achieved.

Citation Download Citation

Lang Wang, Shuxu Guo, and Ruizhi Ren "Hyperspectral image reconstruction based on an improved genetic algorithm", Proc. SPIE 7455, Satellite Data Compression, Communication, and Processing V, 74550K (31 August 2009); https://doi.org/10.1117/12.825603

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