A Novel Supervised Method for Hyperspectral Image Classification with Spectral-Spatial Constraints

查看全文 作　　者：SUN [1]Le;WU [1]Zebin;LIU [1]Jianjun;WEI [1]Zhihui

机构地区：[1]Nanjing University of Science and Technology

出　　处：《Chinese Journal of Electronics》索引2014年第23卷第1期,共7页

基　　金：supported by Jiangsu Provincial Natural Science Foundation of China(No.BK2011701);the National Natural Science Foundation of China(No.61101194);Research Fund for the Doctoral Program of Higher Education of China(No.20113219120024);Project of China Geological Survey(No.1212011120227);Jiangsu Innovation Projects(No.CXZZ12-0206) 摘　　要：In this paper, a new supervised classification method, combining spectral and spatial information,is proposed. The method is based on the two following facts. First, a hyperspectral pixel can be sparsely represented by a linear combination of the dictionary consists of a few labeled samples. If any unknown hyperspectral pixel lies in the subspace spanned by some labeled-class samples, it will be classified to this labeled-class. And this is to solve a fully constrained sparse unmixing problem with the l2 regularization and the criterion of classification is relaxed to be determined by the largest value of sparse vector whose nonzero entries correspond to the weights of the labeled samples. Second, since the nearest neighbors probably belong to the same class, a spatial constraint is introduced. Alternating direction method of multipliers(ADMM) and the graph cut based method are then used to solve the spectral-spatial model. Finally, two real hyperspectral data sets are used to validate our proposed method. Experimental results show that the proposed method outperforms many of the state-of-the-art methods. 关键词：图像分类空间约束高光谱监督稀疏向量空间信息线性组合分解问题

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A Novel Supervised Method for Hyperspectral Image Classification with Spectral-Spatial Constraints

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