| Summary: | 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.
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