Dynamic Learning of SMLR for Feature Selection and Classification of Hyperspectral Data

Abstract

Feature selection is an important task in the analysis of hyperspectral data. Recently developed methods for learning sparse classifiers, which combine the automatic feature selection and classifier design, established themselves among the state of the art in the literature of machine learning. In this letter, the sparse multinomial logistic regression (SMLR) is introduced into the community of remote sensing and is utilized for the feature selection in the classification of hyperspectral data. To relieve the heavy degeneration of classification performance caused by the characteristics of the hyperspectral data and the oversparsity when the SMLR selects a small feature subset, we develop a dynamic learning framework to train the SMLR. Experimental results attest to the effectiveness of the proposed method.