Homogeneous region based low rank representation in hidden field for hyperspectral classification

In this paper, a new classifier under Bayesian framework is proposed to explore homogeneous region based low rank representation in hidden field for classification of hyperspectral imagery (HSI). This classifier integrates low rank representation and superpixel segmentation simultaneously, in which the HSI data is assumed to be lying in a low rank subspace within each homogeneous region of an estimated hidden field. First, the HSI data is projected into the Principal Component space, then the first principal component image is segmented into hundreds of homogeneous regions. Following, the spectral-only supervised Bayesian classifier, i.e., Sparse Multinomial Logistic Regression (SMLR), is utilized for estimating the likelihood probabilities of testing samples, then spatial information is exploited by low rank representation within each superpixel in a hidden field which is approximated to the pre-estimated likelihood probabilities. The proposed model can be easily solved by alternating direction method of multipliers (ADMM). Experimental results on real hyperspectral data, i.e., AVIRIS Indian Pines and ROSIS University of Pavia, show that the proposed classifier outperforms other state-of-the-art classifiers in terms of quantitative assessment and visual effect.