Sparse graph embedding dimension reduction for hyperspectral image with a new spectral similarity metric

Abstract

Graph embedding, as a dimensionality reduction framework, has already drawn great attention in hyperspectral image analysis. Taking locality preserving projection (LPP) as example, LPP utilizes typical Euclidean distance in heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with novel spectral similarity measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA).