Subspace Learning via Local Probability Distribution for Hyperspectral Image Classification

Abstract

The computational procedure of hyperspectral image (HSI) is extremely complex, not only due to the high dimensional information, but also due to the highly correlated data structure. The need of effective processing and analyzing of HSI has met many difficulties. It has been evidenced that dimensionality reduction has been found to be a powerful tool for high dimensional data analysis. Local Fisher’s liner discriminant analysis (LFDA) is an effective method to treat HSI processing. In this paper, a novel approach, called PD-LFDA, is proposed to overcome the weakness of LFDA. PD-LFDA emphasizes the probability distribution (PD) in LFDA, where the maximum distance is replaced with local variance for the construction of weight matrix and the class prior probability is applied to compute the affinity matrix. The proposed approach increases the discriminant ability of the transformed features in low dimensional space. Experimental results on Indian Pines 1992 data indicate that the proposed approach significantly outperforms the traditional alternatives.

Reconstructed Error and Linear Representation Coefficients Restricted by l1‐Minimization for Face Recognition under Different Illumination and Occlusion

“…In recent years, various learning methods are proposed, in which deep learning [27] shows its good performance in pattern recognition. However, other learning methods [28,29] still devote their insight to this field.…”