The use of KPCA over subspaces for cross-scale superpixel based hyperspectral image classification

Abstract

ABSTRACT This paper introduces a new object-based spectral-spatial classification method for hyperspectral image. The kernel principal component analysis (KPCA) is firstly performed over subspaces (KPCAsub) derived from the original spectral domain, which incorporates linear information with nonlinear formulation. The obtained image is then processed via a feature-level fusion with superpixel segmentation at different scales. The final classification result is achieved by a cross-scale superpixel based (CSSP) decision fusion framework based on each individual operation of support vector machine. The resulting method, called KPCAsub-CSSP, contributes to better characterization under-limited sample condition, and promotes spectral-spatial integration in terms of echoing the complex distribution of ground objects. The experimental results on two real hyperspectral data sets demonstrate that the proposed method exhibits good performance in comparison to the other related methods.