Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification
Remote sensing image scene classification is essential, and it can promote the rational planning of land and ecological monitoring in the practical application of agricultural production. High spatial resolution (HSR) remote sensing images are widely used in smart agriculture because of their wide c...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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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/10505774/ |
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| author | Yang Zhao Jiaqi Liang Sisi Huang Pingping Huang |
| author_facet | Yang Zhao Jiaqi Liang Sisi Huang Pingping Huang |
| author_sort | Yang Zhao |
| collection | DOAJ |
| description | Remote sensing image scene classification is essential, and it can promote the rational planning of land and ecological monitoring in the practical application of agricultural production. High spatial resolution (HSR) remote sensing images are widely used in smart agriculture because of their wide coverage and HSR. The HSR remote sensing images have a more detailed description of the local scene. However, the complexity of scene details intensifies the intraclass diversity and interclass similarity of scenes, and the interference to scene classification is more significant. To distinguish scene categories effectively in complex background, this article proposes a scene classification method of remote sensing images based on progressive aggregation (PA) with local and global cooperative learning. Specifically, multilevel local and global feature modules are employed at different levels to describe the influence of local objects and their global distribution on scene category determination. Then, the PA module is introduced to explore the collaboration of the same level features and reduce the interference of shallow redundancy. The residual structure can establish the correlation between multilevel representations, thereby improving the representation of the aggregate features. To verify the performance of the proposed method, we implemented cross-domain experiments on four internationally available remote sensing image classification datasets: NWPU-RESISC45, WHU-RS19, RSSCN7, and AID. The experimental results show that the proposed method is effective and robust in remote sensing scene classification. |
| format | Article |
| id | doaj-art-394bad45907944eb90f09cd9df2f39cb |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-394bad45907944eb90f09cd9df2f39cb2025-08-20T02:05:28ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-01179442945010.1109/JSTARS.2024.339133210505774Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene ClassificationYang Zhao0https://orcid.org/0000-0002-7302-7787Jiaqi Liang1https://orcid.org/0009-0001-2655-2482Sisi Huang2https://orcid.org/0009-0001-1238-0800Pingping Huang3https://orcid.org/0000-0001-7720-1183School of Information Engineering, Inner Mongolia University of Technology, Hohhot, ChinaSchool of Information Engineering, Inner Mongolia University of Technology, Hohhot, ChinaSchool of Information Engineering, Inner Mongolia University of Technology, Hohhot, ChinaSchool of Information Engineering, Inner Mongolia University of Technology, Hohhot, ChinaRemote sensing image scene classification is essential, and it can promote the rational planning of land and ecological monitoring in the practical application of agricultural production. High spatial resolution (HSR) remote sensing images are widely used in smart agriculture because of their wide coverage and HSR. The HSR remote sensing images have a more detailed description of the local scene. However, the complexity of scene details intensifies the intraclass diversity and interclass similarity of scenes, and the interference to scene classification is more significant. To distinguish scene categories effectively in complex background, this article proposes a scene classification method of remote sensing images based on progressive aggregation (PA) with local and global cooperative learning. Specifically, multilevel local and global feature modules are employed at different levels to describe the influence of local objects and their global distribution on scene category determination. Then, the PA module is introduced to explore the collaboration of the same level features and reduce the interference of shallow redundancy. The residual structure can establish the correlation between multilevel representations, thereby improving the representation of the aggregate features. To verify the performance of the proposed method, we implemented cross-domain experiments on four internationally available remote sensing image classification datasets: NWPU-RESISC45, WHU-RS19, RSSCN7, and AID. The experimental results show that the proposed method is effective and robust in remote sensing scene classification.https://ieeexplore.ieee.org/document/10505774/Multilevel local (MLL) and multilevel global (MLG) feature modulesprogressive aggregation (PA)remote sensing images scene classification |
| spellingShingle | Yang Zhao Jiaqi Liang Sisi Huang Pingping Huang Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Multilevel local (MLL) and multilevel global (MLG) feature modules progressive aggregation (PA) remote sensing images scene classification |
| title | Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification |
| title_full | Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification |
| title_fullStr | Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification |
| title_full_unstemmed | Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification |
| title_short | Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification |
| title_sort | hierarchical deep features progressive aggregation for remote sensing images scene classification |
| topic | Multilevel local (MLL) and multilevel global (MLG) feature modules progressive aggregation (PA) remote sensing images scene classification |
| url | https://ieeexplore.ieee.org/document/10505774/ |
| work_keys_str_mv | AT yangzhao hierarchicaldeepfeaturesprogressiveaggregationforremotesensingimagessceneclassification AT jiaqiliang hierarchicaldeepfeaturesprogressiveaggregationforremotesensingimagessceneclassification AT sisihuang hierarchicaldeepfeaturesprogressiveaggregationforremotesensingimagessceneclassification AT pingpinghuang hierarchicaldeepfeaturesprogressiveaggregationforremotesensingimagessceneclassification |