Hyper-LKCNet: Exploring the Utilization of Large Kernel Convolution for Hyperspectral Image Classification
Recently, transformers have garnered significant attention due to their exceptional capability to capture long-range dependencies in data. A critical factor contributing to their superior performance is their ability to operate over large receptive fields. As such, a natural question arises as to ho...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11007459/ |
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| author | Rong Liu Zhilin Li Jiaqi Yang Jian Sun Quanwei Liu |
| author_facet | Rong Liu Zhilin Li Jiaqi Yang Jian Sun Quanwei Liu |
| author_sort | Rong Liu |
| collection | DOAJ |
| description | Recently, transformers have garnered significant attention due to their exceptional capability to capture long-range dependencies in data. A critical factor contributing to their superior performance is their ability to operate over large receptive fields. As such, a natural question arises as to how to expand the receptive fields in convolutional neural networks to achieve the superior performance comparable with that of transformers. Large kernel convolution provides the inspiration for the above issue. To explore the potential of large kernel convolution, we propose a hyperspectral image (HSI) classification algorithm in this article that utilizes a large kernel convolution module combined with multiscale coattention and an adaptive geometric feature (AGF) classifier, named Hyper-LKCNet. By integrating this feature enhancement module, our method effectively adjusts the contributions of various spectral and spatial features, ensuring that the network captures critical but easily overlooked information across both dimensions and improving the performance to classify HSI. The AGF classifier, derived by neural collapse theory, alleviates the sample imbalance problem and incorporates the label smoothing focal loss function to enhance generalization ability. Extensive experiments on four HSI datasets demonstrate that the proposed method outperforms the state-of-the-art approaches. In addition, our algorithm maintains a low parameter count and reduced floating point of operations. |
| format | Article |
| id | doaj-art-be417d8488804b6cb615139dbaf8e2d3 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-be417d8488804b6cb615139dbaf8e2d32025-08-20T02:22:55ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118139501396610.1109/JSTARS.2025.357195411007459Hyper-LKCNet: Exploring the Utilization of Large Kernel Convolution for Hyperspectral Image ClassificationRong Liu0https://orcid.org/0000-0002-4642-9086Zhilin Li1https://orcid.org/0009-0002-5817-5615Jiaqi Yang2https://orcid.org/0000-0001-8322-9270Jian Sun3Quanwei Liu4https://orcid.org/0000-0003-4976-1858School of Geography and Planning, Sun Yat-sen University, Guangzhou, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou, ChinaState Key Laboratory of Information Engineering in Surveying Mapping, and Remote Sensing, Wuhan University, Wuhan, ChinaEnshi Urban Planning and Design Institute Company Ltd., Enshi, ChinaCollege of Science and Engineering, James Cook University, Cairns, QLD, AustraliaRecently, transformers have garnered significant attention due to their exceptional capability to capture long-range dependencies in data. A critical factor contributing to their superior performance is their ability to operate over large receptive fields. As such, a natural question arises as to how to expand the receptive fields in convolutional neural networks to achieve the superior performance comparable with that of transformers. Large kernel convolution provides the inspiration for the above issue. To explore the potential of large kernel convolution, we propose a hyperspectral image (HSI) classification algorithm in this article that utilizes a large kernel convolution module combined with multiscale coattention and an adaptive geometric feature (AGF) classifier, named Hyper-LKCNet. By integrating this feature enhancement module, our method effectively adjusts the contributions of various spectral and spatial features, ensuring that the network captures critical but easily overlooked information across both dimensions and improving the performance to classify HSI. The AGF classifier, derived by neural collapse theory, alleviates the sample imbalance problem and incorporates the label smoothing focal loss function to enhance generalization ability. Extensive experiments on four HSI datasets demonstrate that the proposed method outperforms the state-of-the-art approaches. In addition, our algorithm maintains a low parameter count and reduced floating point of operations.https://ieeexplore.ieee.org/document/11007459/Attention mechanismhyperspectral image (HSI) classificationimbalanced datalarge kernel convolution |
| spellingShingle | Rong Liu Zhilin Li Jiaqi Yang Jian Sun Quanwei Liu Hyper-LKCNet: Exploring the Utilization of Large Kernel Convolution for Hyperspectral Image Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Attention mechanism hyperspectral image (HSI) classification imbalanced data large kernel convolution |
| title | Hyper-LKCNet: Exploring the Utilization of Large Kernel Convolution for Hyperspectral Image Classification |
| title_full | Hyper-LKCNet: Exploring the Utilization of Large Kernel Convolution for Hyperspectral Image Classification |
| title_fullStr | Hyper-LKCNet: Exploring the Utilization of Large Kernel Convolution for Hyperspectral Image Classification |
| title_full_unstemmed | Hyper-LKCNet: Exploring the Utilization of Large Kernel Convolution for Hyperspectral Image Classification |
| title_short | Hyper-LKCNet: Exploring the Utilization of Large Kernel Convolution for Hyperspectral Image Classification |
| title_sort | hyper lkcnet exploring the utilization of large kernel convolution for hyperspectral image classification |
| topic | Attention mechanism hyperspectral image (HSI) classification imbalanced data large kernel convolution |
| url | https://ieeexplore.ieee.org/document/11007459/ |
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