Abstract：Hyperspectral image(Hyperspectral image,HSI)has not only spatial information but also spectral dimension information compared to ordinary images.It has high requirements for transmission bandwidth in data transmission because of storage.The use of compressed sensing theory can ease the transmission and storage pressure of HSI to a large extent.This paper proposes an OMP algorithm(MO-OMP)based on fast non-dominated sorting III optimization to reconstruct HSI.By considering the spatial and the similarity of hyperspectral images,the reconstruction process of hyperspectral image compressed sensing is modeled with Many-objectives and the NSGA-III algorithm is used to solve the model,which improves the accuracy of the model.In this paper,the algorithm is tested on three sets of public data sets.The experimental results show that the MO-OMP algorithm has a good effect on the problem of hyperspectral image compressed sensing.The Many-objective compressed sensing model,compared with the traditional hyperspectral image compressed sensing model,has more robust in the reconstruction of hyperspectral images.
张景波,蔡星娟,谢丽萍. 高光谱图像的高维多目标压缩感知技术研究[J]. 小型微型计算机系统, 2022, 43(10): 2150-2156.
ZHANG Jing-bo,CAI Xing-juan,XEI Li-ping. Research on Compressed Sensing of Hyperspectral Image with Many-objective Optimization. Journal of Chinese Computer Systems, 2022, 43(10): 2150-2156.
2022, Vol. 43 
