一种高光谱图像的双压缩感知模型

doi:10.7527/S1000-6893.2014.0350

Abstract

Abstract:

It is challenging for hyperspectral images to store, transport and subsequently analyze and process in terms of its huge amounts of data. Compressed sensing theory provides a new signal collection framework. A double compressive sampling and reconstruction model is proposed based on the three-dimensional characteristics of hyperspectral data. During the sampling stage, in terms of the sparsity of hyperspectral data between spatial and spectral, a sensing matrix is constructed to carry out spatial and spectral compressive sampling simultaneously. At the reconstruction stage, the proposed algorithm is different from the traditional reconstruction methods of compressed sensing, with which hyperspectral data are reconstructed directly; instead, with the proposed method, hyperspectral data are separated into endmembers and abundances to reconstruct respectively, then hyperspectral data are generated by reconstructed endmembers and abundances. Experimental results show that the reconstruction accuracy of double compressed sensing is improved by more than 10 dB under low sampling rate sampling, compared with three-dimensional compressive sampling, furthermore the computing speed is ascended by 3 orders of magnitude. Meanwhile, as a byproduct, endmembers and abundances can be obtained conveniently.

Key words: hyperspectral imagery, compressed sensing, data compression, linear mixing model, endmember extraction, abundance estimation

CLC Number:

FENG Yan, WANG Zhongliang, WANG Li. A double compressed sensing model of hyperspectral imagery[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(9): 3041-3049.

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A double compressed sensing model of hyperspectral imagery

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