Title: Adaptive group Riemannian manifold learning for hyperspectral image classification

Authors: Haoxiang Tao; Xiaofeng Xie; Rongnian Tang; Yao Hou; Jingru Li; Wen Feng; Youlong Chen; Gaodi Xu

Addresses: The Mechanical and Electrical Engineering Colleges, Hainan University, Haikou, Hainan, China ' The Mechanical and Electrical Engineering Colleges, Hainan University, Haikou, Hainan, China ' The Mechanical and Electrical Engineering Colleges, Hainan University, Haikou, Hainan, China ' The Mechanical and Electrical Engineering Colleges, Hainan University, Haikou, Hainan, China ' The Mechanical and Electrical Engineering Colleges, Hainan University, Haikou, Hainan, China ' Hainan Meteorological Observatory, Haikou, Hainan, China ' Hainan Meteorological Observatory, Haikou, Hainan, China ' Hainan Association for Artificial Intelligence, Haikou, Hainan, China

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.

Keywords: hyperspectral image; Riemannian manifold; classification; dimensionality reduction; adaptive selection.

DOI: 10.1504/IJWMC.2022.124821

International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.3/4, pp.300 - 309

Received: 03 Sep 2021
Accepted: 17 Jan 2022
Published online: 09 Aug 2022 *

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