Multiscale Superpixel Kernel-Based Low-Rank Representation for Hyperspectral Image Classification

Abstract

Classification plays an important role in the field of hyperspectral image (HSI) remote sensing. In this letter, a novel multiscale superpixel kernel-based low-rank representation (MSKLRR) classifier is proposed for HSI classification. A multiscale superpixel segmentation method is first used to generate several homogeneous regions at different scales. Then, the multiscale superpixel spectral-spatial kernel (SSK) is generated using the radial basis function (RBF) kernel on the multiscale superpixels. Finally, the multiscale superpixel kernel is integrated into a low-rank representation (LRR) to generate the MSKLRR classifier for HSI classification. The experimental results with two widely used HSIs suggest an advantage of the proposed method over other classical classification methods.