基于超像素和低秩的协同稀疏高光谱解混

为了克服经典协同稀疏解混算法的不足以及全变差正则项引起的边缘模糊问题,同时考虑到稀疏性和空间信息对解混精度提高的重要性,采用结合超像素和低秩的协同稀疏高光谱解混算法,进行了理论分析和实验验证.该算法对高光谱图像进行超像素分割,并对每个超像素施加协同稀疏性约束.此外使用低秩正则项代替传统的全变差正则项来利用空间信息,选取...

Superpixels and low rank for collaborative sparse hyperspectral unmixing

To overcome the shortcomings of the classic collaborative sparse unmixing algorithm and the edge blur problem caused by the total variation regular term,considering the importance of sparsity and spatial information to improve the accuracy of unmixing,a novel algorithm called superpixel and low rank for collaborative sparse unmixing was proposed.The unmixing algorithm was theoretically analyzed and experimentally verified.The superpixel segmentation was performed on the hyperspectral images,and then collaborative sparsity constraints were imposed on each superpixel.In addition,a low-rank regular term was used instead of the traditional total variation regular term to utilize spatial information.A set of simulated data and a set of real data were selected for experiments.These results show that the signal reconstruction error obtained in the simulated experiment is 19.4 when the signal-to-noise ratio is 30dB,which is about 35%higher than that of the classic sparse unmixing via variable splitting augmented Lagrangian and total variation algorithm.Real data experiment intuitively reflects that the algorithm can effectively overcome the problem of edge blur.The proposed algorithm has better unmixing performance.This research provides a reference for how to use sparsity and spatial information comprehensively.