Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review
Class imbalance is a very challenging problem in data science, affecting the development of several application fields. This problem also plagues the automatic interpretation of remote sensing images. Especially in tasks such as classification mapping, object detection, change detection, and scene c...
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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/10945429/ |
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| _version_ | 1850149899143741440 |
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| author | Pengdi Chen Yuanrui Ren Baoan Zhang Yuan Zhao |
| author_facet | Pengdi Chen Yuanrui Ren Baoan Zhang Yuan Zhao |
| author_sort | Pengdi Chen |
| collection | DOAJ |
| description | Class imbalance is a very challenging problem in data science, affecting the development of several application fields. This problem also plagues the automatic interpretation of remote sensing images. Especially in tasks such as classification mapping, object detection, change detection, and scene classification, the classes of training samples required by machine learning exhibit uneven distribution, which seriously affects the accuracy of model training. Our meta-analysis is based on 171 journal papers retrieved and screened from the Web of Science database, covering publication years, highly productive countries, highly cited authors, remote sensing data types, data augmentation methods, and the distribution of the main application fields. The solution to the proposed problem involves three aspects: model innovation and optimization, loss function improvement, and data augmentation. Experiments on benchmark datasets have demonstrated the effectiveness of these methods. In terms of remote sensing task applications, we provide a comprehensive review and analysis of recent research cases on deep learning aimed at addressing the class imbalance problem. Finally, we discuss the synergistic relationship between models, loss functions, and data augmentation, summarize the current challenges in this field, as well as propose several ideas for addressing the class imbalance problem. |
| format | Article |
| id | doaj-art-fccc2da4976b4fc2b993a3bb37447730 |
| 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-fccc2da4976b4fc2b993a3bb374477302025-08-20T02:26:45ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01189483950810.1109/JSTARS.2025.355556710945429Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A ReviewPengdi Chen0https://orcid.org/0009-0008-4777-7120Yuanrui Ren1Baoan Zhang2Yuan Zhao3Lanzhou University, Lanzhou, ChinaLanzhou University, Lanzhou, ChinaMapping Institution of Gansu Province, Lanzhou, ChinaMapping Institution of Gansu Province, Lanzhou, ChinaClass imbalance is a very challenging problem in data science, affecting the development of several application fields. This problem also plagues the automatic interpretation of remote sensing images. Especially in tasks such as classification mapping, object detection, change detection, and scene classification, the classes of training samples required by machine learning exhibit uneven distribution, which seriously affects the accuracy of model training. Our meta-analysis is based on 171 journal papers retrieved and screened from the Web of Science database, covering publication years, highly productive countries, highly cited authors, remote sensing data types, data augmentation methods, and the distribution of the main application fields. The solution to the proposed problem involves three aspects: model innovation and optimization, loss function improvement, and data augmentation. Experiments on benchmark datasets have demonstrated the effectiveness of these methods. In terms of remote sensing task applications, we provide a comprehensive review and analysis of recent research cases on deep learning aimed at addressing the class imbalance problem. Finally, we discuss the synergistic relationship between models, loss functions, and data augmentation, summarize the current challenges in this field, as well as propose several ideas for addressing the class imbalance problem.https://ieeexplore.ieee.org/document/10945429/Automatic interpretationclass imbalancedeep learningmeta-analysisremote sensing images |
| spellingShingle | Pengdi Chen Yuanrui Ren Baoan Zhang Yuan Zhao Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Automatic interpretation class imbalance deep learning meta-analysis remote sensing images |
| title | Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review |
| title_full | Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review |
| title_fullStr | Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review |
| title_full_unstemmed | Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review |
| title_short | Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review |
| title_sort | class imbalance in the automatic interpretation of remote sensing images a review |
| topic | Automatic interpretation class imbalance deep learning meta-analysis remote sensing images |
| url | https://ieeexplore.ieee.org/document/10945429/ |
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