基于空间约束的高光谱图像解混算法

Hyperspectral image unmixing method based on spatial constraint

基于表征相邻像元之间的同质性和差异性的空间约束,对稀疏解混模型进行改进,提高高光谱图像混合像元解混的精度。压缩原始光谱库生成解混光谱库,提高了空间约束对解混模型的影响,降低了高光谱图像对解混光谱库的稀疏性。基于解混光谱库,根据高光谱图像局部空域内的同质性和差异性构建流形约束项,将其以正则项形式与稀疏解混模型相结合构建改进解混模型,利用交替方向乘子法对改进后的解混模型进行凸优化求解。实验结果表明,算法对高光谱图像具有高的解混精度和优异的抗噪声性能。

The sparse unmixing model is improved based on the spatial constraint which represents the similarity and difference between the adjacent pixels.Thus,the accuracy of the hyperspectral image unmixing is increased.The unmixing spectrum library is generated by compressing the primary spectrum library to increase the influence of the spatial constraint on the unmixing model,and reduce the sparsity of hyperspectral image in the unmixing spectrum library.According to unmixing spectrum library,the improved unmixing model is constructed by sparse unmixing and manifold regularization,which represent the similarity and difference between the adjacent pixels.The improved unmixing model is solved by means of the convex optimization algorithm such as alternating directions method of multipliers.Experimental results show that the proposed algorithm has high spectral unmixing accuracy and strong performance.