基于子空间结构正则化的L_(21)非负矩阵分解高光谱解混  

L_(21) Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Subspace Structure Regularization

作　　者：陈善学[1] 刘荣华 CHEN Shanxue;LIU Ronghua(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Chongqing Key Laboratory of Mobile Communications Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065 [2]移动通信技术重庆市重点实验室,重庆400065

出　　处：《电子与信息学报》2022年第5期1704-1713,共10页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61271260);重庆市教委科学技术研究项目(KJ1400416)。

摘　　要：标准的非负矩阵分解(NMF)应用于高光谱解混时,容易受到噪声和异常值的干扰,解混效果较差。为了提高分解性能,该文将L_(21)范数引入标准的NMF算法中,对模型进行了改进,从而提高算法的鲁棒性。其次,为了提高分解后丰度矩阵的稀疏性,将双重加权稀疏约束引入L_(21)NMF模型中,使其中一个权值提高每个像元对应的丰度向量上的稀疏性,另一个权值提高每个端元对应的丰度向量上的稀疏性。同时,为了利用像元的全局空间分布信息,观察地物在不同图像中的真实分布情况,引入子空间结构正则项,提出了基于子空间结构正则化的L_(21)非负矩阵分解(L_(21)NMF-SSR)算法。通过在模拟数据集和真实数据集与其他经典算法的比较,验证了该算法具有更好的性能,同时具有去噪能力。When the standard Nonnegative Matrix Factorization(NMF)is applied to hyperspectral unmixing,it is easy to be interfered by noise and outliers,and the unmixing effect is poor.In order to improve the factorized performance,the L_(21) norm is introduced into the standard NMF algorithm,and the model is improved to improve the robustness of the algorithm.Secondly,in order to improve the sparsity of the factorized abundance matrix,the double reweighted sparse constraint is introduced into the L_(21)NMF model,so that one of the weights increases sparsity along the abundance vector corresponding to each pixel,and the other weight promotes the sparsity along the abundance vector corresponding to each endmember.Meanwhile,in order to utilize the global spatial distribution information of the pixels and observe the true distribution of materials in different images,the subspace structure regularization is introduced,and the L_(21) Nonnegative Matrix Factorization based on Subspace Structure Regularization(L_(21)NMF-SSR)is proposed.The better performance and denoising ability of the proposed method are demonstrated by comparing with other classical methods on both synthetic and real datasets.

关 键 词：高光谱解混 非负矩阵分解 L_(21)范数 双重加权稀疏 子空间结构 

分 类 号：TN911.73[电子电信—通信与信息系统]