A lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction.
Hyperspectral Image (HSI) classification tasks are usually impacted by Convolutional Neural Networks (CNN). Specifically, the majority of models using traditional convolutions for HSI classification tasks extract redundant information due to the convolution layer, which makes the subsequent network...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0322345 |
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| author | Yuping Yin Haodong Zhu Lin Wei |
| author_facet | Yuping Yin Haodong Zhu Lin Wei |
| author_sort | Yuping Yin |
| collection | DOAJ |
| description | Hyperspectral Image (HSI) classification tasks are usually impacted by Convolutional Neural Networks (CNN). Specifically, the majority of models using traditional convolutions for HSI classification tasks extract redundant information due to the convolution layer, which makes the subsequent network structure produce a large number of parameters and complex computations, so as to limit their classification effectiveness, particularly in situations with constraints on computational power and storage capacity. To address these issues, this paper proposes a lightweight multi-layer feature fusion classification method for hyperspectral images based on spatial and channel reconstruction (SCNet). Firstly, this method reduces redundant computations of spatial and spectral features by introducing Spatial and Channel Reconstruction Convolutions (SCConv), a novel convolutional compression method. Secondly, the proposed network backbone is stacked with multiple SCConv modules, which allows the network to capture spatial and spectral features that are more beneficial for hyperspectral image classification. Finally, to effectively utilize the multi-layer feature information generated by SCConv modules, a multi-layer feature fusion (MLFF) unit was designed to connect multiple feature maps at different depths, thereby obtaining a more robust feature representation. The experimental results demonstrate that, compared to seven other hyperspectral image classification methods, this network has significant advantages in terms of the number of parameters, model complexity, and testing time. These findings have been validated through experiments on four benchmark datasets. |
| format | Article |
| id | doaj-art-d7a6dc7d0e0848999e08bd87c0febaa7 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-d7a6dc7d0e0848999e08bd87c0febaa72025-08-20T03:48:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032234510.1371/journal.pone.0322345A lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction.Yuping YinHaodong ZhuLin WeiHyperspectral Image (HSI) classification tasks are usually impacted by Convolutional Neural Networks (CNN). Specifically, the majority of models using traditional convolutions for HSI classification tasks extract redundant information due to the convolution layer, which makes the subsequent network structure produce a large number of parameters and complex computations, so as to limit their classification effectiveness, particularly in situations with constraints on computational power and storage capacity. To address these issues, this paper proposes a lightweight multi-layer feature fusion classification method for hyperspectral images based on spatial and channel reconstruction (SCNet). Firstly, this method reduces redundant computations of spatial and spectral features by introducing Spatial and Channel Reconstruction Convolutions (SCConv), a novel convolutional compression method. Secondly, the proposed network backbone is stacked with multiple SCConv modules, which allows the network to capture spatial and spectral features that are more beneficial for hyperspectral image classification. Finally, to effectively utilize the multi-layer feature information generated by SCConv modules, a multi-layer feature fusion (MLFF) unit was designed to connect multiple feature maps at different depths, thereby obtaining a more robust feature representation. The experimental results demonstrate that, compared to seven other hyperspectral image classification methods, this network has significant advantages in terms of the number of parameters, model complexity, and testing time. These findings have been validated through experiments on four benchmark datasets.https://doi.org/10.1371/journal.pone.0322345 |
| spellingShingle | Yuping Yin Haodong Zhu Lin Wei A lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction. PLoS ONE |
| title | A lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction. |
| title_full | A lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction. |
| title_fullStr | A lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction. |
| title_full_unstemmed | A lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction. |
| title_short | A lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction. |
| title_sort | lightweight hyperspectral image multi layer feature fusion classification method based on spatial and channel reconstruction |
| url | https://doi.org/10.1371/journal.pone.0322345 |
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