Hyperspectral Remote Sensing Images Terrain Classification Based on Direct LFDA

Abstract

AbstractAiming at the problem of small sample size (SSS) and multimodal structure of hyperspectral remote sensing data, a feature extraction method of hyperspectral remote sensing images (HRSIs) combining direct linear discriminant analysis (LDA) and local Fisher discriminant analysis (LFDA) was proposed. Firstly, the local within-class scatter matrix and local between-class scatter matrix of the samples are calculated by LFDA method; then the projection matrix is solved by referring to the idea of direct LDA method, i.e., the local between-class scatter matrix is whitened first, and then the local within-class scatter matrix is diagonalized so that the zero-space of the local within-class scatter matrix which containing classification information is retained, so as to better maintaining the separability of the data while preserving the multimodal structure of data in the projection subspace. Finally, the minimum distance classifier is used to classify and recognize in the direct LFDA feature subspace. The experimental results of two real HRSIs show that compared with LDA, direct LDA and LFDA, the proposed direct LFDA algorithm can effectively improve the terrain recognition accuracy.KeywordsHyperspectral Remote Sensing Images (HRSIs)Terrain classificationFeature extractionLocal Fisher Discriminant Analysis (LFDA)