Construction of sparse basis by dictionary training for compressive sensing hyperspectral imaging

As a novel imaging theoretical, compressive sensing (CS) hyperspectral imaging utilizes the sparse property of the earth objects to efficiently obtain the hyperspectral cube with much less data volume. The construction of sparse basis is of great importance for CS hyperspectral imaging. In this paper, a spectral sparse basis construction method based on earth object´s spectral library and redundant dictionary training is proposed. Compared with traditional DCT and wavelet basis, the sparse basis constructed by our method performs much better in simulation experiments.