Hyperspectral data compression using sparse representation

Abstract

Due to all bands of hyperspectral data have the same imaging area, it is reasonable to believe that the dictionary can sparse represent one band may also represent the other bands sparsely. Based on this property, this paper presents a new compression frame for hyperspectral data using sparse representation, and a simplified algorithm under this frame is also proposed. The basic idea of the proposed algorithm is to sparse coding bands using the dictionary learned from one training band, and its innovation is that patches having the same spatial location of all bands are restricted to be represented using the same atoms. Experimental results based on OMP and K-SVD are provided, which reveal that this proposal has better performance than wavelet based compression algorithm at low bit rates.