Research on Compression Perceptual Hyperspectral Image Reconstruction Based on GISMT

Abstract

Hyperspectral images contain rich information, including diversity of space, radiation and spectrum information. However, the huge amount of information also causes the problem of large amount of hyperspectral image data, which makes the hyperspectral image data face a lot of problems in the aspects of transmission and storage. To solve this problem, a compression sensing hyperspectral image reconstruction algorithm based on GISMT is proposed. This paper first introduces the improved joint sparse representation model, and then, based on the improved joint sparse representation model, designs a joint sparse representation solution algorithm based on GISA, GISMT, simulation results table. This method can improve the reconstruction effect of hyperspectral remote sensing image.