Hyperspectral Image Classification via Superpixel Correlation Coefficient Representation

Abstract

Superpixel segmentation is a powerful tool that can effectively utilize spectral and spatial information for hyperspectral image (HSI) classification. In this paper, we propose a new HSI classification method via superpixel and correlation coefficient (CC) representation (SCCR), where CC is a powerful metric to assess similarity among pixels. Considering the correlation among pixels within each superpixel, the superpixel region is viewed as the neighborhood of a test sample that can better exploit the spatial structure information of HSI. In the proposed method, recursive filtering is first used to extract features for hyperspectral data analysis. Then, based on the $k$ -nearest neighbors theory, the CC between each test sample and each class of training samples can be jointly calculated by the $k$ most relevant pixels in the neighborhood, which effectively reduces the effect of the mixed samples from different classes on the classification accuracy. Finally, the class label of each test sample is determined by which class presents the maximum CC. The experimental results demonstrate that the proposed SCCR improves classification performance compared to other well-known classification methods.