MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images
The effectiveness of training-based object detection heavily depends on the amount of sample data. But in the field of remote sensing, the amount of sample data is difficult to meet the needs of network training due to the non-cooperative imaging modes and complex imaging conditions. Moreover, the i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/17/4201 |
| _version_ | 1827727877718671360 |
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| author | Huan Zhang Wei Leng Xiaolin Han Weidong Sun |
| author_facet | Huan Zhang Wei Leng Xiaolin Han Weidong Sun |
| author_sort | Huan Zhang |
| collection | DOAJ |
| description | The effectiveness of training-based object detection heavily depends on the amount of sample data. But in the field of remote sensing, the amount of sample data is difficult to meet the needs of network training due to the non-cooperative imaging modes and complex imaging conditions. Moreover, the imbalance of the sample data between different categories may lead to the long-tail problem during the training. Given that similar sensors, data acquisition approaches, and data structures could make the targets in different categories possess certain similarities, those categories can be modeled together within a subspace rather than the entire space to leverage the amounts of sample data in different subspaces. To this end, a subspace-dividing strategy and a subspace-based multi-branch network is proposed for object detection in remotely sensed images. Specifically, a combination index is defined to depict this kind of similarity, a generalized category consisting of similar categories is proposed to represent the subspace, and a new subspace-based loss function is devised to address the relationship between targets in one subspace and across different subspaces to integrate the sample data from similar categories within a subspace and to balance the amounts of sample data between different subspaces. Furthermore, a subspace-based multi-branch network is constructed to ensure the subspace-aware regression. Experiments on the DOTA and HRSC2016 datasets demonstrated the superiority of our proposed method. |
| first_indexed | 2024-03-10T23:14:11Z |
| format | Article |
| id | doaj.art-38cb0aea97ff4c90985d9c1a6ecaac0e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:14:11Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-38cb0aea97ff4c90985d9c1a6ecaac0e2023-11-19T08:45:51ZengMDPI AGRemote Sensing2072-42922023-08-011517420110.3390/rs15174201MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed ImagesHuan Zhang0Wei Leng1Xiaolin Han2Weidong Sun3Department of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaThe effectiveness of training-based object detection heavily depends on the amount of sample data. But in the field of remote sensing, the amount of sample data is difficult to meet the needs of network training due to the non-cooperative imaging modes and complex imaging conditions. Moreover, the imbalance of the sample data between different categories may lead to the long-tail problem during the training. Given that similar sensors, data acquisition approaches, and data structures could make the targets in different categories possess certain similarities, those categories can be modeled together within a subspace rather than the entire space to leverage the amounts of sample data in different subspaces. To this end, a subspace-dividing strategy and a subspace-based multi-branch network is proposed for object detection in remotely sensed images. Specifically, a combination index is defined to depict this kind of similarity, a generalized category consisting of similar categories is proposed to represent the subspace, and a new subspace-based loss function is devised to address the relationship between targets in one subspace and across different subspaces to integrate the sample data from similar categories within a subspace and to balance the amounts of sample data between different subspaces. Furthermore, a subspace-based multi-branch network is constructed to ensure the subspace-aware regression. Experiments on the DOTA and HRSC2016 datasets demonstrated the superiority of our proposed method.https://www.mdpi.com/2072-4292/15/17/4201object detectionlong-tail problemgeneralized categorysubspace-based multi-branch networkremotely sensed image |
| spellingShingle | Huan Zhang Wei Leng Xiaolin Han Weidong Sun MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images Remote Sensing object detection long-tail problem generalized category subspace-based multi-branch network remotely sensed image |
| title | MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images |
| title_full | MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images |
| title_fullStr | MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images |
| title_full_unstemmed | MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images |
| title_short | MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images |
| title_sort | moon a subspace based multi branch network for object detection in remotely sensed images |
| topic | object detection long-tail problem generalized category subspace-based multi-branch network remotely sensed image |
| url | https://www.mdpi.com/2072-4292/15/17/4201 |
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