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Spectral-Spatial Constraint Hyperspectral Image Classification

高光谱成像 像素 模式识别(心理学) 人工智能 特征(语言学) 计算机科学 空间分析 特征向量 约束(计算机辅助设计) 图像(数学) 数学 计算机视觉 统计 几何学 语言学 哲学
作者
Rongrong Ji,Yue Gao,Richang Hong,Qiong Liu,Dacheng Tao,Xuelong Li
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:52 (3): 1811-1824 被引量:199
标识
DOI:10.1109/tgrs.2013.2255297
摘要

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.
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