Spectral-spatial classification of Hyperspectral images using approximate sparse multinomial logistic regression

Abstract

We propose the sparse multinomial logistic regression (SMLR) model for spectral-spatial classification of hyperspectral images. In the proposed method, the parameters of SMLR are iteratively estimated from logposterior by using Laplace approximation. The proposed update rule provides a faster convergence compared to the state-of the-art methods used for SMLR parameter estimation. The estimated parameters are used for spectralspatial classification of hyperspectral images using a spatial prior. The experimental results on real hyperspectral images show that the classification accuracy of proposed method is also better than those of state-of-the art methods. © 2017 Elsevier B.V., All rights reserved.