A Kernel Spectral Angle Mapper algorithm for remote sensing image classification

Abstract

A Kernel Spectral Angle Mapper (KSAM) algorithm is proposed to deal better with the nonlinear classification problem of the remote sensing image. The so-called KSAM algorithm is achieved by introducing the kernel method into the standard Spectral Angle Mapper (SAM) algorithm. Experimental results indicate that the classification accuracy of the KSAM algorithm is superior to one of the SAM algorithm in the remote sensing image classification. However the kernel parameters of the polynomial and sigmoid kernel functions of the algorithm are excessively sensitive. A narrow bound of the kernel parameters in the polynomial and sigmoid kernel functions can be chosen for the optimal classification of the remote sensing image. The classification performance of the Radial Basis Function (RBF) kernel function is superior to one of the polynomial and sigmoid kernel functions. A wide bound of the kernel parameter in the RBF kernel function can be chosen for the optimal classification of the remote sensing image in the KSAM algorithm.