Spectral-Spatial Constraint Hyperspectral Image Classification

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	Spectral-Spatial Constraint Hyperspectral Image Classification
	Ji, Rongrong ; Gao, Yue ; Hong, Richang ; Liu, Qiong ; Tao, Dacheng ; Li, Xuelong ; Ji RR(纪荣嵘)
刊名	http://dx.doi.org/10.1109/TGRS.2013.2255297
	2014
关键词	MORPHOLOGICAL PROFILES COMPONENT ANALYSIS FEATURE-SELECTION SVM RECOGNITION INFORMATION FEATURES BAND
英文摘要	National Basic Research Program of China 973 Program [2012CB316400]; 985 Project of Xiamen University; National Natural Science Foundation of China [61125106, 91120302, 61072093]; Shaanxi Key Innovation Team of Science and Technology [2012KCT-04]; Hyperspectral image classification has attracted extensive research efforts in the recent decade. The main difficulty lies in the few labeled samples versus the high dimensional features. To this end, it is a fundamental step to explore the relationship among different pixels in hyperspectral image classification, toward jointly handing both the lack of label and high dimensionality problems. In the hyperspectral images, the classification task can be benefited from the spatial layout information. In this paper, we propose a hyperspectral image classification method to address both the pixel spectral and spatial constraints, in which the relationship among pixels is formulated in a hypergraph structure. In the constructed hypergraph, each vertex denotes a pixel in the hyperspectral image. And the hyperedges are constructed from both the distance between pixels in the feature space and the spatial locations of pixels. More specifically, a feature-based hyperedge is generated by using distance among pixels, where each pixel is connected with its K nearest neighbors in the feature space. Second, a spatial-based hyperedge is generated to model the layout among pixels by linking where each pixel is linked with its spatial local neighbors. Both the learning on the combinational hypergraph is conducted by jointly investigating the image feature and the spatial layout of pixels to seek their joint optimal partitions. Experiments on four data sets are performed to evaluate the effectiveness and and efficiency of the proposed method. Comparisons to the state-of-the-art methods demonstrate the superiority of the proposed method in the hyperspectral image classification.
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92725]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Ji, Rongrong,Gao, Yue,Hong, Richang,et al. Spectral-Spatial Constraint Hyperspectral Image Classification[J]. http://dx.doi.org/10.1109/TGRS.2013.2255297,2014.
APA	Ji, Rongrong.,Gao, Yue.,Hong, Richang.,Liu, Qiong.,Tao, Dacheng.,...&纪荣嵘.(2014).Spectral-Spatial Constraint Hyperspectral Image Classification.http://dx.doi.org/10.1109/TGRS.2013.2255297.
MLA	Ji, Rongrong,et al."Spectral-Spatial Constraint Hyperspectral Image Classification".http://dx.doi.org/10.1109/TGRS.2013.2255297 (2014).