DocumentCode :
1760684
Title :
Spectral-Spatial Constraint Hyperspectral Image Classification
Author :
Rongrong Ji ; Yue Gao ; Richang Hong ; Qiong Liu ; Dacheng Tao ; Xuelong Li
Author_Institution :
Dept. of Cognitive Sci., Xiamen Univ., Xiamen, China
Volume :
52
Issue :
3
fYear :
2014
fDate :
41699
Firstpage :
1811
Lastpage :
1824
Abstract :
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.
Keywords :
hyperspectral imaging; image classification; high dimensional features; high dimensionality problems; hypergraph structure; hyperspectral image classification method; image feature; pixel spatial constraint; pixel spatial layout; pixel spatial locations; pixel spectral constraint; spatial layout information; state-of-the-art methods; Hypergraph learning; hyperspectral; image classification; spatial-constraint;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2013.2255297
Filename :
6527932
Link To Document :
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