Spectral dimensional correlation and sparse reconstruction model of hyperspectral images

Abstract

The high dimensionality and redundancy properties of hyperspectral image (HSI) constitute great challenges for transmission and process. Recently, HSI reconstruction based on compressed sensing has received increasing attention and has become a frontier problem. Effectively exploiting the sparse prior of HSI is crucial to improve the reconstruction quality. In this paper, the spectral dimension plane of the HSI band group is defined, and the structural correlation of HSI spectral dimension is studied. The following conclusions can be obtained. First, the texture distribution of the HSI spectral dimension is simpler and more uniform than that of the HIS spatial domain, and its texture exhibits low contrast and high properties, thus simplifying the sparse representation process. Second, in the HSI spectral dimension plane, the search area adjacent to the reference block has a certain number of spectral curve blocks that are highly similar to the reference block. Based on this observation, the structure correlation of the HIS spectral dimension is defined, and a sparse measurement model is proposed. Finally, the sparse model S-SCoSM is proposed by integrating the sparse representation of spatial dimension nonlocal correlation and the spectral dimension structure correlation. The sparse reconstruction model of HSI is constructed using this sparse constrain prior. Experimental results show that by further exploring the correlation of HSI from the viewpoint of spatial and spectral domains, the proposed sparse model S-SCoSM obtains a more adequate and effective HSI sparse constraint prior; hence, the reconstruction quality of HSI is improved. Consequently, the spatial information quality of the reconstructed band image can be effectively improved as well as the spectral attributes of band groups can be well maintained.