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In this paper we develop a new approach for recovery and unmixing of the compressed hyperspectral image. The sparsity of the signal is the key property for the success of the reconstruction. We propose a new sparse representation making use of the existing spectral library as a prior. Specificly, a linear mixing model is employed and we substitute the mixing matrix with the spectral library. As a...
Different from traditional Nyquist sampling theorem, compressive sensing realizes sampling and compressing signals at the same time. The design of sensing matrix plays an implicit role in compressive sensing. In order to improve the quality of reconstruction, we propose the optimal methods to sensing matrix based on matrix decomposition: the SVD method and the QR method. Since the larger the minimum...
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