The training accuracy and loss curves of SRU using different normalization methods on the UP dataset.

The training accuracy and loss curves of SRU using different normalization methods on the UP dataset.

<p>The training accuracy and loss curves of SRU using different normalization methods on the UP dataset.</p>

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Bibliographic Details
Main Author:	Yuping Yin (3617510) (author)
Other Authors:	Haodong Zhu (14412015) (author), Lin Wei (70005) (author)
Published:	2025
Subjects:	Cell Biology Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified robust feature representation hyperspectral images based experimental results demonstrate convolutional neural networks layer feature fusion hyperspectral image classification proposed network backbone channel reconstruction convolutions multiple scconv modules channel reconstruction scconv modules convolution layer scconv ), classification tasks classification effectiveness usually impacted thereby obtaining testing time storage capacity spectral features significant advantages scnet ). paper proposes model complexity effectively utilize different depths computational power complex computations cnn ).
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