Spatial-spectral compressive sensing of hyperspectral image

Compressive sensing (CS) is a new emerging approach in recent years, and is applied in acquisition of signals having a sparse or compressible representation in some basis. The CS literature has mostly focused on the problems involving 1-D signals and 2-D images. However, for hyperspectral image, compressive acquisition of this signal is complicated for its 3-D structures. In this paper, we consider the correlation of spatial and spectral of hyperspectral image and propose spatial-spectral compressive sensing. The results show that the proposed method leads to an increase in CS reconstruction performance under the same compression ratio and reconstruction algorithm. In particular, our method is more advantageous in realizing airborne or spaceborne hyperspectral remote sensing for its lower memory storage.