GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification
Deep convolutional neural networks have become an indispensable method in remote sensing image scene classification because of their powerful feature extraction capabilities. However, the ability of the models to extract multiscale features and global features on surface objects of complex scenes is...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9678028/ |
| _version_ | 1797947285026373632 |
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| author | Weitao Chen Shubing Ouyang Wei Tong Xianju Li Xiongwei Zheng Lizhe Wang |
| author_facet | Weitao Chen Shubing Ouyang Wei Tong Xianju Li Xiongwei Zheng Lizhe Wang |
| author_sort | Weitao Chen |
| collection | DOAJ |
| description | Deep convolutional neural networks have become an indispensable method in remote sensing image scene classification because of their powerful feature extraction capabilities. However, the ability of the models to extract multiscale features and global features on surface objects of complex scenes is currently insufficient. We propose a framework based on global context spatial attention (GCSA) and densely connected convolutional networks to extract multiscale global scene features, called GCSANet. The mixup operation is used to enhance the spatial mixed data of remote sensing images, and the discrete sample space is rendered continuous to improve the smoothness in the neighborhood of the data space. The characteristics of multiscale surface objects are extracted, and their internal dense connection is strengthened by the densely connected backbone network. GCSA is introduced into the densely connected backbone network to encode the context information of the remote sensing scene image into the local features. Experiments were performed on four remote sensing scene datasets to evaluate the performance of GCSANet. The GCSANet achieved the highest classification precision on AID and NWPU datasets and the second-best performance on the UC Merced dataset, indicating the GCSANet can effectively extract the global features of remote sensing images. In addition, the GCSANet presents the highest classification accuracy on the constructed mountain image scene dataset. These results reveal that the GCSANet can effectively extract multiscale global scene features on complex remote sensing scenes. The source codes of this method can be foundin <uri>https://github.com/ShubingOuyangcug/GCSANet</uri>. |
| first_indexed | 2024-04-10T21:25:24Z |
| format | Article |
| id | doaj.art-bec92b7db0fb426080b08072628066dc |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-10T21:25:24Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-bec92b7db0fb426080b08072628066dc2023-01-20T00:00:21ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01151150116210.1109/JSTARS.2022.31418269678028GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene ClassificationWeitao Chen0https://orcid.org/0000-0002-6272-1618Shubing Ouyang1https://orcid.org/0000-0003-4737-4205Wei Tong2https://orcid.org/0000-0003-2873-7584Xianju Li3https://orcid.org/0000-0001-7785-2541Xiongwei Zheng4Lizhe Wang5https://orcid.org/0000-0003-2766-0845School of Computer Science, China University of Geosciences, Wuhan, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, ChinaDeep convolutional neural networks have become an indispensable method in remote sensing image scene classification because of their powerful feature extraction capabilities. However, the ability of the models to extract multiscale features and global features on surface objects of complex scenes is currently insufficient. We propose a framework based on global context spatial attention (GCSA) and densely connected convolutional networks to extract multiscale global scene features, called GCSANet. The mixup operation is used to enhance the spatial mixed data of remote sensing images, and the discrete sample space is rendered continuous to improve the smoothness in the neighborhood of the data space. The characteristics of multiscale surface objects are extracted, and their internal dense connection is strengthened by the densely connected backbone network. GCSA is introduced into the densely connected backbone network to encode the context information of the remote sensing scene image into the local features. Experiments were performed on four remote sensing scene datasets to evaluate the performance of GCSANet. The GCSANet achieved the highest classification precision on AID and NWPU datasets and the second-best performance on the UC Merced dataset, indicating the GCSANet can effectively extract the global features of remote sensing images. In addition, the GCSANet presents the highest classification accuracy on the constructed mountain image scene dataset. These results reveal that the GCSANet can effectively extract multiscale global scene features on complex remote sensing scenes. The source codes of this method can be foundin <uri>https://github.com/ShubingOuyangcug/GCSANet</uri>.https://ieeexplore.ieee.org/document/9678028/Attention mechanismfeature channelglobal context informationremote sensingscene classification |
| spellingShingle | Weitao Chen Shubing Ouyang Wei Tong Xianju Li Xiongwei Zheng Lizhe Wang GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Attention mechanism feature channel global context information remote sensing scene classification |
| title | GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification |
| title_full | GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification |
| title_fullStr | GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification |
| title_full_unstemmed | GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification |
| title_short | GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification |
| title_sort | gcsanet a global context spatial attention deep learning network for remote sensing scene classification |
| topic | Attention mechanism feature channel global context information remote sensing scene classification |
| url | https://ieeexplore.ieee.org/document/9678028/ |
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