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- W2890982797 abstract "Hyperspectral images usually have high-dimensional and abundant spectral information for land-cover types classification. Research shows that multiple kinds of features would be helpful to the classification task. In this paper, a new feature fusion framework—deep multiple feature fusion (DMFF)—is proposed for the classification of the hyperspectral image. First, several different features are extracted for each pixel. Then, these features are fed to a deep random forest classifier. With a multiple-layer structure, the outputs of preceding layers will be used as the inputs of the subsequent layers. After the final layer, the classification probability will be computed. By introducing the information of neighboring pixels, the spectral–spatial information is combined effectively. Besides, the structure of the DMFF is easy to expand. Experimental results based on two widely used hyperspectral datasets (Indian pines image and Pavia University image) demonstrate that the proposed method can achieve a satisfactory classification performance compared with other multiple feature fusion methods." @default.
- W2890982797 created "2018-09-27" @default.
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- W2890982797 date "2018-10-01" @default.
- W2890982797 modified "2023-09-23" @default.
- W2890982797 title "Deep Multiple Feature Fusion for Hyperspectral Image Classification" @default.
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- W2890982797 doi "https://doi.org/10.1109/jstars.2018.2866595" @default.
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