典型文献
Low-Rank and Sparse Representation with Adaptive Neighborhood Regulari-zation for Hyperspectral Image Classification
文献摘要：
Low-Rank and Sparse Representation ( LRSR ) method has gained popularity in Hyperspectral Image ( HSI ) processing. However, existing LRSR models rarely exploited spectral-spatial classification of HSI. In this paper, we proposed a novel Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization ( LRSR-ANR ) method for HSI classification. In the proposed method, we first represent the hyperspectral data via LRSR since it combines both sparsity and low-rankness to maintain global and local data structures simultaneously. The LRSR is optimized by using a mixed Gauss-Seidel and Jacobian Alternating Direction Method of Multipliers ( M-ADMM) , which converges faster than ADMM. Then to incorporate the spatial information, an ANR scheme is designed by combining Euclidean and Cosine distance metrics to reduce the mixed pixels within a neighborhood. Lastly, the predicted labels are determined by jointly considering the homogeneous pixels in the classification rule of the minimum reconstruction error. Experimental results based on three popular hyperspectral images demonstrate that the proposed method outperforms other related methods in terms of classification accuracy and generalization performance.
文献关键词：
中图分类号：
作者姓名：
Zhaohui XUE;Xiangyu NIE
作者机构：
School of Earth Sciences and Engineering,Hohai University,Nanjing 211100,China
文献出处：
引用格式：
[1]Zhaohui XUE;Xiangyu NIE-.Low-Rank and Sparse Representation with Adaptive Neighborhood Regulari-zation for Hyperspectral Image Classification)[J].测绘学报（英文版）,2022(01):73-90
A类：
Regulari,LRSR,rankness
B类：
Low,Rank,Sparse,Representation,Adaptive,Neighborhood,Hyperspectral,Image,Classification,has,gained,popularity,HSI,processing,However,existing,models,rarely,exploited,spatial,classification,In,this,paper,proposed,novel,Regularization,ANR,first,represent,hyperspectral,data,via,since,combines,both,sparsity,low,maintain,global,local,structures,simultaneously,optimized,by,using,mixed,Gauss,Seidel,Jacobian,Alternating,Direction,Method,Multipliers,ADMM,which,converges,faster,than,Then,incorporate,information,scheme,designed,combining,Euclidean,Cosine,distance,metrics,reduce,pixels,within,neighborhood,Lastly,predicted,labels,determined,jointly,considering,homogeneous,rule,minimum,reconstruction,error,Experimental,results,three,images,demonstrate,that,outperforms,other,related,methods,terms,accuracy,generalization,performance
AB值：
0.582385
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