Spectral-Spatial Classification of Hyperspectral Images Using Approximate Sparse Multinomial Logistic Regression

摘要

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 log-posterior 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 spectral-spatial 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.