期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 57, 期 2, 页码 1142-1154出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2018.2864983
关键词
Gabor surface feature (GSF); hyperspectral imagery classification
类别
资金
- National Natural Science Foundation of China [61671307]
- Guangdong Special Support Program of Top-notch Young Professionals [2015TQ01X238]
- Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources [KF-2016-02-002]
- Shenzhen Future Industry Development Funding Program [201607281039561400]
Since the spatial distribution of surface materials is usually regular and locally continuous, it is reasonable to utilize the spectral and spatial information for the hyperspectral image classification. In this paper, a spectral-spatial Gabor surface feature (GSF) fusion approach has been proposed for hyperspectral image classification. First, Gabor magnitude pictures (GMPs) are extracted by applying a set of predefined 2-D Gabor filters to hyperspectral images. Second, the GSF has been extended to the spectral-spatial domains to comply with the 3-D structure of hyperspectral imagery, called 3-DGSF, which utilizes the first-order derivative of GMPs. Meanwhile, a classic superpixel segmentation method, called simple linear iterative clustering (SLIC), is adopted to divide the original hyperspectral image into disjoint superpixels. Third, principal component analysis is adopted to reduce the dimensionality of each extracted 3-DGSF feature cube. Next, a support vector machine classifier is applied on each reduced 3-DGSF features, and the majority voting strategy is used to obtain the classification results. Finally, the superpixel map obtained by SLIC is used to regularize the classification map, and thus, the proposed approach is named as S3-DGSF. Extensive experiments on three real hyperspectral data sets have demonstrated the higher performance of the proposed S3-DGSF approach over several state-of-the-art methods in the literature.
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