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- W2897555495 abstract "In this letter, it is proposed the hyperspectral image classification method based on the convolutional neural network, which is trained jointly by the reconstruction and discriminative loss functions. In the network, small convolutional kernels are cascaded with the pooling operator to perform feature abstraction, and a decoding channel composed of the deconvolutional and unpooling operators is established. The unsupervised reconstruction, performed by the decoding channel, not only introduces priors to the network training but also is made use to enhance the discriminability of the abstracted features by the control gate. By the experiments, it is shown that the proposed method performs better than the state-of-the-art neural network-based classification methods." @default.
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- W2897555495 date "2019-03-01" @default.
- W2897555495 modified "2023-10-15" @default.
- W2897555495 title "Convolutional Neural Network Trained by Joint Loss for Hyperspectral Image Classification" @default.
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- W2897555495 doi "https://doi.org/10.1109/lgrs.2018.2872359" @default.
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