Hyperspectral Image Classification by Using Pixel Spatial Correlation

Yue Gao, Tat-Seng Chua

Abstract: This paper introduces a hyperspectral image classification approach by using pixel spatial relationship. In hyperspectral images, the spatial relationship among pixels has been shown to be important in the exploration of pixel labels. To better employ the spatial information, we propose to estimate the correlation among pixels in a hypergraph structure. In the constructed hypergraph, each pixel is denoted by a vertex, and the hyperedge is constructed by using the spatial neighbors of each pixel. Semi-supervised learning on the constructed hypergraph is conducted for hyperspectral image classification. Experiments on two datasets are used to evaluate the performance of the proposed method. Comparisons with the state-of-the-art methods demonstrate that the proposed method can effectively investigate the spatial relationship among pixels and achieve better hyperspectral image classification results.