Diverse-Region Hyperspectral Image Classification via Superpixelwise Graph Convolution Technique.

In this paper, a diverse-region hyperspectral image classification (DRHy) method is proposed by considering both irregularly local pixels and globally contextual connections between pixels. Specifically, the proposed method is operated on non-Euclidean graphs, which are constructed by superpixel segmentation methods for diverse regions to cluster irregularly local-region pixels. In addition, the dimensionality reduction method is employed to alleviate the curse of dimensionality problem with a lower computational burden, generating more representative data with the input graph features. In this context, it then constructs a superpixelwise Chebyshev polynomial graph convolution network (ChebyNet) to aggregate global-region superpixels. Benefiting from different superpixel numbers of segmentations, we construct different graph structures, and multiple classification results are obtained, which brings more opportunities to represent the hyperspectral data correctly. Then, all the diverse-region results are further fused by a majority voting technique to improve the final performance. Finally, numerical experiments on two benchmark datasets are provided to demonstrate the superiority of the proposed DRHy-ChebyNet method to the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]