Compressed Sensing Reconstruction of Hyperspectral Images Based on Spectral Unmixing

How to utilize the characteristics of hyperspectral images (HSIs) is a key problem in application of compressed sensing theory to hyperspectral image compression and reconstruction. Based on the study of spectral mixing characteristics, a compressed sensing reconstruction algorithm with spectral unmixing for HSIs is proposed. Taking advantage of linear mixing model, the HSIs are separated into endmember matrix and abundance matrix. Instead of directly reconstructing the entire hyperspectral data as traditional reconstruction algorithms, the proposed algorithm explores the idea of spectral unmixing for reconstruction. In the sampling process, the HSIs are sampled both spatially and spectrally. In the reconstruction process, a joint optimization problem for endmember extraction and abundance estimation is established and solved in an iterative way to obtain the reconstructed hyperspectral data. Experimental results on synthetic and real hyperspectral data demonstrate that the proposed algorithm could obtain the endmember and abundance information effectively, and the accuracy of reconstructed HSIs as well as the computational efficiency are superior to the state-of-the-art reconstruction algorithms.