Import Vector Machine Based Hyperspectral Imagery Classification

Some deficiencies still exist in the support vector machine (SVM) based classification. For example, model training takes long time; the number of support vectors changes along with the number of training samples, resulting in poor stability and sparsity. In this paper, we describe a novel import vector machine (IVM) based approach that can achieve sparsity and improve the efficiency and accuracy for hyperspectral imagery classification. On the basis of kernel logistic regression model, IVM used the greedy forward algorithm to choose import vector from training samples for model training. The proposed approach is tested on the PHI data and performance comparison shows better stability and stronger sparsity of the proposed approach over support vector machine based classification.