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- W2985312688 abstract "Generally, the traditional supervised hyperspectral image (HSI) classification cannot fully exploit spatial and spectral features simultaneously. In this paper, we reformulate HSI feature learning in terms of sparse separable convolutional filter learning problem and propose a sparse separable convolutional classification model (SSCCM). In the proposed SSCCM, the sparse separable convolutional learning module (SSCLM) is used to extract robust spatial-spectral features and utilizes rank-one tensor decomposion learning mechanism to accelerate feature computation. While the SVM classification module (SVMCM) employs the 3D spatial-spectral feature array to represent the HSI for classification. Experimental results on the widely used HSI datasets demonstrate the superior performance of our proposed approach over the state-of-theart classification methods." @default.
- W2985312688 created "2019-11-22" @default.
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- W2985312688 date "2019-07-01" @default.
- W2985312688 modified "2023-09-26" @default.
- W2985312688 title "Supervised Hyperspectral Image Classification Via Sparse Separable Convolutional Feature Learning" @default.
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- W2985312688 doi "https://doi.org/10.1109/igarss.2019.8899162" @default.
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