关键词: 稀疏分解, 压缩感知, 高光谱遥感图像, Gabor原子特征贪心聚类算法

Abstract:

In order to improve sampling of the compression coding technology and constantly improve the imaging spectrometer systems, based on the principles of the sparse decomposition and compressed sensing, hyperspectral remote sensing images were compressed and reconstructed. A fast sparse decomposition algorithm of the image decomposition on over\|complete atomic library was proposed so as to reduce the amount of computing. The simulation results show that the calculation improved processing speed and reduced costs by spectral compression of hyperspectral images carried out with the compressed sensing and bands grouping technology.

Key words: sparse decomposition, compressed sensing (CS), hyperspectral remote sensing images, Gabor atomic greedy clustering algorithm (GAGCA)