Hyperspectral Classification Via Low-Rank Component Induced Spatial-Spectral Kernel

Abstract

Spatial-spectral kernel (SSK) has proven to be one of state-of-the-art tools for producing precise classification results for hyperspectral images (HSIs). However, how to exactly identify the neighborhood pixels within a given cubic patch of HSI is one of the critical tasks for constructing an accurate spatial-spectral kernel (SSK). In this paper, a novel low-rank component induced SSK (LRCISSK) method is proposed to deliver more accurate classification results for HSI. It explores the low-rank properties within each HSI patch in spectral domain to adaptively identify the precise neighborhood pixels with regards to the centroid pixel. Then, the neighborhood pixels associated with the centroid pixel are embedded into the SSK framework to easily map the spectra into the nonlinear complex manifolds and enable the support vector machine (SVM) classifier to effectively discriminate them. Experiments on Indian Pines and Pavia University datasets demonstrate the superiority of the proposed LRCISSK classifier when compared to other state-of-the-art approaches.