期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 57, 期 12, 页码 10336-10349出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2933588
关键词
Image edge detection; Smoothing methods; Feature extraction; Support vector machines; Transforms; Hyperspectral imaging; Decision fusion; edge-preserving operation (EPO); feature extraction; hyperspectral image (HSI); image classification
类别
资金
- Major Program of the National Natural Science Foundation of China [61890962]
- National Natural Science Foundation of China [61601179, 6187119]
- National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]
- Fund of the Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province [2018TP1013]
- Fund of Hunan Province for the Science and Technology Plan Project [2017RS3024]
In this article, a novel hyperspectral image (HSI) classification method based on fusing multiple edge-preserving operations (EPOs) is proposed, which consists of the following steps. First, the edge-preserving features are obtained by performing different types of EPOs, i.e., local edge-preserving filtering and global edge-preserving smoothing on the dimension-reduced HSI. Then, with the assistance of a superpixel segmentation method, the edge-preserving features are further improved by considering the inter and intra spectral properties of superpixels. Finally, the spectral and edge-preserving features are fused to form one composite kernel, which is fed into the support vector machine (SVM) followed by a majority voting fusion scheme. Experimental results on three data sets demonstrate the superiority of the proposed method over several state-of-the-art classification approaches, especially when the training sample size is limited. Furthermore, 21 well-known methods, including mathematical morphology-based approaches, sparse representation models, and deep learning-based classifiers, are adopted to be compared with the proposed method on Houston data set with standard sets of training and test samples released during 2013 Data Fusion Contest, which also shows the effectiveness of the proposed method.
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