Cauchy Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Graph Laplacian Regularization
Non-negative matrix decomposition(NMF)based on Euclidean distance standard is easy to cause unmixing failure in hyperspectral images with noise and abnormal pixel pollution.In order to suppress the influence of noise or abnormal pixels,the NMF model based on Cauchy loss function is adopted to improve the robustness of unmixing.Because the suppression of outliers may destroy the intrinsic abundance structure of hyperspectral images.Therefore,in order to ensure that the original hyperspectral internal data is not destroyed,the graph Laplace constraint is introduced into the model.At the same time,in order to improve the sparsity of abundance matrix and improve the performance of unmixing,a reweighted sparse constraint term is introduced,and a decomposition algorithm of Cauchy non-negative matrix based on graph Laplacian regularization(CNMF-GLR)is proposed.Considering the requirement of Laplacian constraint on neighborhood selection,this paper uses local neighborhood weighting method to determine local neighborhood by rectangular window structure.By comparing with other classical algorithms with the same initialization conditions on simulated and real data sets,the proposed algorithm is proved to have better robustness and unmixing performance.
Key worlds hyperspectral unmixingnonnegative matrix factorizationCauchy loss functiongraph Laplacianreweight sparse
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