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Remote Sens. 2016, 8(9), 748; doi:10.3390/rs8090748

Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model

Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Author to whom correspondence should be addressed.
Academic Editors: Soe Myint, Xiaofeng Li and Prasad S. Thenkabail
Received: 21 July 2016 / Revised: 19 August 2016 / Accepted: 5 September 2016 / Published: 11 September 2016
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Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In this method, a novel technique for labeling regions obtained by the spectral-spatial segmentation process is presented. Our method includes the following steps. First, the probabilistic support vector machines (SVM) classifier is used to estimate probabilities belonging to each information class. Second, an extended JBF is employed to perform image smoothing on the probability maps. By using our JBF process, salt-and-pepper classification noise in homogeneous regions can be effectively smoothed out while object boundaries in the original image are better preserved as well. Third, a sequence of modified bi-labeling graph cut models is constructed for each information class to extract the desirable object belonging to the corresponding class from the smoothed probability maps. Finally, a classification map is achieved by merging the segmentation maps obtained in the last step using a simple and effective rule. Experimental results based on three benchmark airborne hyperspectral datasets with different resolutions and contexts demonstrate that our method can achieve 8.56%–13.68% higher overall accuracies than the pixel-wise SVM classifier. The performance of our method was further compared to several classical hyperspectral image classification methods using objective quantitative measures and a visual qualitative evaluation. View Full-Text
Keywords: hyperspectral images; classification; spectral-spatial; graph cut; joint bilateral filter hyperspectral images; classification; spectral-spatial; graph cut; joint bilateral filter

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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Wang, Y.; Song, H.; Zhang, Y. Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model. Remote Sens. 2016, 8, 748.

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