Multi-manifold Locality Graph Preserving Analysis for Hyperspectral Image Classification

Abstract Manifold learning has been successfully applied to hyperspectral image (HSI) classification by modeling different land covers as a smooth manifold embedded in a high-dimensional space. However, traditional manifold learning algorithms were proposed with the assumption of single manifold structure in HSI, while the samples in different subsets may belong to different sub-manifolds. In this paper, a novel dimensionality reduction (DR) method called multi-manifold locality graph preserving analysis (MLGPA) was proposed for feature learning of HSI data. According to the label information of HSI, MLGPA divides the samples data into different subsets, and each subset is treated as a sub-manifold. Then, it constructs a within-manifold graph and a between-manifold graph for each sub-manifold to characterize within-manifold compactness and between-manifold separability, and a discriminant projection matrix can be obtained by maximizing the between-manifold scatter and minimizing the within-manifold scatter simultaneously. Finally, low-dimensional embedding features of different sub-manifolds are fused to improve the classification performance. MLGPA can effectively reveal the multi-manifold structure and improve the classification performance of HSI. Experimental results on three real-world HSI data sets demonstrate that MLGPA is superior to some state-of-the-art methods in terms of classification accuracy.