Adaptive group Riemannian manifold learning for hyperspectral image classification

Abstract

Hyperspectral image classification is an important topic in the field of remote sensing. However, the high dimensionality and high spatial-spectral correlation of hyperspectral image will easily lead to poor classification performance due to the Hughes phenomenon. In this paper, we proposed a adaptive group local Riemannian embedding, called AGLRE, to extract the spatial and spectral features from hyperspectral image. It firstly mapped original data into a Riemannian manifold by constructing region covariance matrices for each pixel of hyperspectral image. And the multiple local tangent space on Riemannian manifold were learned by adaptive neighbourhood strategy. Lastly, the local linear embedding was applied to reduce the dimensionality and merge multiple tangent space into a global coordinate. Experimental results on public hyperspectral data set showed that the proposed method can achieve higher classification performance than other competing algorithms.