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

Fubiao Feng, Qiong Ran*, Wei Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 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.

Original languageEnglish
Pages (from-to)22959-22977
Number of pages19
JournalMultimedia Tools and Applications
Volume76
Issue number21
DOIs
Publication statusPublished - 1 Nov 2017
Externally publishedYes

Keywords

  • D-S evidence theory
  • Dimensionality reduction
  • Graph embedding
  • Hyperspectral data
  • Multi-level fusion

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