Shape-Dependent Dynamic Label Assignment for Oriented Remote Sensing Object Detection
Oriented remote sensing object detection (ORSOD) has gained increasing significance in both military and civilian applications due to the necessity of accurately identifying objects with varying shapes and orientations in remote sensing data. Traditional ORSOD methods often employ fixed label assign...
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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/10745646/ |
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| author | Xue Zhang Yanxia Wu Guoyin Zhang Ye Yuan Guangliang Cheng Yulei Wu |
| author_facet | Xue Zhang Yanxia Wu Guoyin Zhang Ye Yuan Guangliang Cheng Yulei Wu |
| author_sort | Xue Zhang |
| collection | DOAJ |
| description | Oriented remote sensing object detection (ORSOD) has gained increasing significance in both military and civilian applications due to the necessity of accurately identifying objects with varying shapes and orientations in remote sensing data. Traditional ORSOD methods often employ fixed label assignment strategies to differentiate between positive and negative samples. However, most of them frequently overlook the impact of object shape on sample quality, leading to an imbalanced distribution of positive samples and exacerbating the inconsistency between classification and regression tasks, thereby limiting detection performance. To address these challenges, we propose a novel shape-dependent assignment (SDA) method that dynamically differentiates positive and negative samples based on object shape. It introduces a new metric for evaluating sample box quality by considering angular differences relative to ground truth (GT) boxes and adjusts the sample scoring threshold according to the aspect ratio of each GT box. In addition, we present a DIoU-adaptive weighting (DAW) module that enhances the interaction between classification and regression tasks by leveraging the distance-IoU metric. This approach not only balances the quantity of samples but also improves their quality, enabling more effective training schemes for samples of varying qualities. We validate our proposed methods through extensive experiments on three challenging ORSOD datasets: DOTA-1.0, HRSC2016, and UCAS-AOD. The results demonstrate that our approach achieves significant improvements, especially for objects with large aspect ratios. |
| format | Article |
| id | doaj-art-7e70fa480de6470da4aa1951af7e21e8 |
| 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-7e70fa480de6470da4aa1951af7e21e82025-08-20T02:07:24ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011813214610.1109/JSTARS.2024.349216410745646Shape-Dependent Dynamic Label Assignment for Oriented Remote Sensing Object DetectionXue Zhang0https://orcid.org/0009-0004-7567-8614Yanxia Wu1https://orcid.org/0000-0001-8384-9234Guoyin Zhang2https://orcid.org/0000-0001-6925-3039Ye Yuan3https://orcid.org/0000-0002-8210-5054Guangliang Cheng4https://orcid.org/0000-0002-1428-8848Yulei Wu5https://orcid.org/0000-0003-0801-8443College of Computer Science and Technology, Harbin Engineering University, Harbin, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin, ChinaCollege of Computer Science and Technology, Harbin Engineering University, Harbin, ChinaDepartment of Computer Science, University of Liverpool, Liverpool, U.K.Faculty of Science and Engineering, University of Bristol, Bristol, U.K.Oriented remote sensing object detection (ORSOD) has gained increasing significance in both military and civilian applications due to the necessity of accurately identifying objects with varying shapes and orientations in remote sensing data. Traditional ORSOD methods often employ fixed label assignment strategies to differentiate between positive and negative samples. However, most of them frequently overlook the impact of object shape on sample quality, leading to an imbalanced distribution of positive samples and exacerbating the inconsistency between classification and regression tasks, thereby limiting detection performance. To address these challenges, we propose a novel shape-dependent assignment (SDA) method that dynamically differentiates positive and negative samples based on object shape. It introduces a new metric for evaluating sample box quality by considering angular differences relative to ground truth (GT) boxes and adjusts the sample scoring threshold according to the aspect ratio of each GT box. In addition, we present a DIoU-adaptive weighting (DAW) module that enhances the interaction between classification and regression tasks by leveraging the distance-IoU metric. This approach not only balances the quantity of samples but also improves their quality, enabling more effective training schemes for samples of varying qualities. We validate our proposed methods through extensive experiments on three challenging ORSOD datasets: DOTA-1.0, HRSC2016, and UCAS-AOD. The results demonstrate that our approach achieves significant improvements, especially for objects with large aspect ratios.https://ieeexplore.ieee.org/document/10745646/High-quality sampleslabel assignmentobjects with large aspect ratiosoriented remote sensing object detection (ORSOD) |
| spellingShingle | Xue Zhang Yanxia Wu Guoyin Zhang Ye Yuan Guangliang Cheng Yulei Wu Shape-Dependent Dynamic Label Assignment for Oriented Remote Sensing Object Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing High-quality samples label assignment objects with large aspect ratios oriented remote sensing object detection (ORSOD) |
| title | Shape-Dependent Dynamic Label Assignment for Oriented Remote Sensing Object Detection |
| title_full | Shape-Dependent Dynamic Label Assignment for Oriented Remote Sensing Object Detection |
| title_fullStr | Shape-Dependent Dynamic Label Assignment for Oriented Remote Sensing Object Detection |
| title_full_unstemmed | Shape-Dependent Dynamic Label Assignment for Oriented Remote Sensing Object Detection |
| title_short | Shape-Dependent Dynamic Label Assignment for Oriented Remote Sensing Object Detection |
| title_sort | shape dependent dynamic label assignment for oriented remote sensing object detection |
| topic | High-quality samples label assignment objects with large aspect ratios oriented remote sensing object detection (ORSOD) |
| url | https://ieeexplore.ieee.org/document/10745646/ |
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