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- W4312097002 abstract "This paper proposed a relatively lightweight DL model for the classification of hyperspectral imagery, which is built on the PCANet backbone framework and combined with residual connection. Compared to the conventional CNNs, the proposed DL model (Hereafter dubbed ‘ResPCANet’) has some surprising merits, on the one hand, more simpler of network architecture and less requirements of the labelled samples, as well as faster learning speed of the network. On the other hand, combined with residual skip connection, more diverse and complementary features that derived from different layers can be fused for subsequent classifier construction. Our experiments, carried out on real hyperspectral dataset, revealed the effectiveness of the proposed ResPCANet model, which further demonstrated the superiority of the residual skip connection strategy." @default.
- W4312097002 created "2023-01-04" @default.
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- W4312097002 date "2022-11-05" @default.
- W4312097002 modified "2023-09-28" @default.
- W4312097002 title "PCANet Combined with Residual Connection for Hyperspectral Image Classification" @default.
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- W4312097002 doi "https://doi.org/10.1109/cisp-bmei56279.2022.9980165" @default.
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