Spectral and spatial classification of hyperspectral images based on random multi-graphs

Feng Gao, Qun Wang, Junyu Dong*, Qizhi Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)

Abstract

Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods.

Original languageEnglish
Article number1271
JournalRemote Sensing
Volume10
Issue number8
DOIs
Publication statusPublished - 1 Aug 2018
Externally publishedYes

Keywords

  • Hyperspectral image
  • Local binary patterns
  • Pattern classification
  • Random multi-graphs

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