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

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Abstract

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 ℓ 1-norm, ℓ 2-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.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grants No. NSFC-61571033, 61302164, 61501017 and partly by the Fundamental Research Funds for the Central Universities under Grants No. BUCTRC201401, BUCTRC201615, YS1404, XK1521, ZY1504.

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Authors and Affiliations

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China

    Fubiao Feng, Qiong Ran & Wei Li

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  1. Fubiao Feng
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  2. Qiong Ran
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  3. Wei Li
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Correspondence to Qiong Ran.

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Feng, F., Ran, Q. & Li, W. Multi-level fusion of graph based discriminant analysis for hyperspectral image classification. Multimed Tools Appl 76, 22959–22977 (2017). https://doi.org/10.1007/s11042-016-4183-7

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  • DOI: https://doi.org/10.1007/s11042-016-4183-7

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