2023
DOI: 10.3390/rs16010022
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A Hybrid-Scale Feature Enhancement Network for Hyperspectral Image Classification

Dongxu Liu,
Tao Shao,
Guanglin Qi
et al.

Abstract: Due to their devastating ability to extract features, convolutional neural network (CNN)-based approaches have achieved tremendous success in hyperspectral image (HSI) classification. However, previous works have been dedicated to constructing deeper or wider deep learning networks to obtain exceptional classification performance, but as the layers get deeper, the gradient disappearance problem impedes the convergence stability of network models. Additionally, previous works usually focused on utilizing fixed-… Show more

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Cited by 2 publications
(1 citation statement)
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“…The module enabled the target features to be effectively obtained by the proposed model, while also distinguishing the differences in features between different positions, improving the detection accuracy of the model and enhancing its stability and robustness. The shuffle attention module could improve the learning performance of the model, and similar conclusions can be found in the article [27].…”
Section: Discussionsupporting
confidence: 77%
“…The module enabled the target features to be effectively obtained by the proposed model, while also distinguishing the differences in features between different positions, improving the detection accuracy of the model and enhancing its stability and robustness. The shuffle attention module could improve the learning performance of the model, and similar conclusions can be found in the article [27].…”
Section: Discussionsupporting
confidence: 77%