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RSS FeedsRemote Sensing, Vol. 10, Pages 1271: Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs (Remote Sensing)

 
 

15 august 2018 10:01:21

 
Remote Sensing, Vol. 10, Pages 1271: Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs (Remote Sensing)
 




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.


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20 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 10, Pages 1272: Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series (Remote Sensing)
Remote Sensing, Vol. 10, Pages 1270: Greening and Browning of the Hexi Corridor in Northwest China: Spatial Patterns and Responses to Climatic Variability and Anthropogenic Drivers (Remote Sensing)
 
 
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