A Band-Weighted Support Vector Machine Method for Hyperspectral Imagery Classification

Abstract

A band-weighted support vector machine (BWSVM) method is proposed to classify hyperspectral imagery (HSI). The BWSVM presents an L1 penalty term of band weight vector to regularize the regular SVM model. The L1 norm regularization term guarantees the sparsity of band weights and describes potentially divergent contributions from different bands in modeling the binary SVM model. The BWSVM adopts the KerNel iterative feature extraction algorithm to minimize the nonconvex program. It linearizes nonlinear kernels and iteratively optimizes two convex subproblems with respect to both sample coefficients and band weights. The class label is determined by picking the largest sample coefficients from all its binary models of BWSVM. Two popular HSI data sets are utilized to testify the classification performance of BWSVM. Experimental results show that the BWSVM outperforms three state-of-the-art classifiers including SVM, random forest, and k-nearest neighbor.