Multi-level fusion of graph based discriminant analysis for hyperspectral image classification

Based on the graph-embedding framework, sparse graph-based discriminant analysis (SGDA), collaborative graph-based discriminant analysis (CGDA) and low rankness graph based discriminant analysis (LGDA) have been proposed with different graph construction. However, due to the inherent characteristics of ℓ₁-norm, ℓ₂-norm and nuclear-norm, single graph may be not optimal in capturing global and local structure of the data. In this paper, a multi-level fusion strategy is proposed in combining the three graph construction methods: 1) multiple graphs-based discriminant analysis (MGDA) in feature level with adaptive weights; 2) decision level fusion with D-S theory (GDA-DS), followed by a typical support vector machine (SVM) classification. Experimental results on three hyperspectral images datasets demonstrate that results with the fused strategy prevails with better classification performance.