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Remote Sens. 2018, 10(8), 1271;

Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs

1,2,* and 3
College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Qingdao Key Laboratory of Mixed Reality and Virtual Ocean, Ocean University of China, Qingdao 266100, China
Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Author to whom correspondence should be addressed.
Received: 10 July 2018 / Revised: 5 August 2018 / Accepted: 9 August 2018 / Published: 12 August 2018
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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. View Full-Text
Keywords: random multi-graphs; local binary patterns; hyperspectral image; pattern classification random multi-graphs; local binary patterns; hyperspectral image; pattern classification

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Gao, F.; Wang, Q.; Dong, J.; Xu, Q. Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs. Remote Sens. 2018, 10, 1271.

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