期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 60, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3091860
关键词
Feature extraction; Image segmentation; Hyperspectral imaging; Training; Classification algorithms; Task analysis; Geometry; Curvature filter (CF); dimensionality reduction; hyperspectral image (HSI) classification; image segmentation; multiscale feature extraction
类别
资金
- National Natural Science Fund of China [61890962, 61801178, 61871179]
- Science and Technology Talents Program of Hunan Association for Science and Technology [2017TJ-Q09]
- Scientific Research Project of Hunan Education Department [19B105]
A curvature filters-based multiscale feature extraction method with multiscale superpixel segmentation constraint is proposed for hyperspectral image classification. Experimental results demonstrate significant improvement in classification accuracies compared to standard methods, especially in cases of limited training samples.
Exploring fast and effective spectral-spatial feature extraction algorithms for hyperspectral image (HSI) classification is one of the most focus problems in current hyperspectral remote-sensing research. Generally, the size of homogeneous regions in HSIs is not consistent in real scenario and real scenario usually consist of ground objects of different scales. Multiscale strategy starts to be used to construct discriminative features at different scales for HSI classification in recent years. To efficiently characterize the multiscale spectral-spatial features of HSIs, a curvature filters-based multiscale feature extraction method with multiscale superpixel segmentation constraint is proposed. The proposed algorithm is composed of the following major stages. First, global multiscale spectral-spatial features are efficiently extracted via progressively curvature filtering and downsampling operations, which can be regarded as an image pyramid decomposition method. Next, a multiscale superpixel segmentation strategy is applied on the first layer of the image pyramid, and a weighted mean operation is applied within and among superpixels to extract the local multiscale spatial features (LMSFs). Finally, the global multiscale curvature features (GMCFs) and the superpixel segmentation-based LMSFs are fused to form the final multiscale spectral-spatial features for classification purposes. To verify the capabilities of the proposed method, comprehensive experiments are performed on five real hyperspectral datasets. Experimental results demonstrate that the proposed method can significantly improve the classification accuracies compared to several standard HSI feature extraction and classification methods, especially when the number of samples for training is limited.
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