Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images
Oriented object detection in remote sensing images (RSIs) is a significant yet challenging Earth Vision task, as the objects in RSIs usually emerge with complicated backgrounds, arbitrary orientations, multi-scale distributions, and dramatic aspect ratio variations. Existing oriented object detector...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/18/3622 |
| _version_ | 1797517391161196544 |
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| author | Xu He Shiping Ma Linyuan He Le Ru Chen Wang |
| author_facet | Xu He Shiping Ma Linyuan He Le Ru Chen Wang |
| author_sort | Xu He |
| collection | DOAJ |
| description | Oriented object detection in remote sensing images (RSIs) is a significant yet challenging Earth Vision task, as the objects in RSIs usually emerge with complicated backgrounds, arbitrary orientations, multi-scale distributions, and dramatic aspect ratio variations. Existing oriented object detectors are mostly inherited from the anchor-based paradigm. However, the prominent performance of high-precision and real-time detection with anchor-based detectors is overshadowed by the design limitations of tediously rotated anchors. By using the simplicity and efficiency of keypoint-based detection, in this work, we extend a keypoint-based detector to the task of oriented object detection in RSIs. Specifically, we first simplify the oriented bounding box (OBB) as a center-based rotated inscribed ellipse (RIE), and then employ six parameters to represent the RIE inside each OBB: the center point position of the RIE, the offsets of the long half axis, the length of the short half axis, and an orientation label. In addition, to resolve the influence of complex backgrounds and large-scale variations, a high-resolution gated aggregation network (HRGANet) is designed to identify the targets of interest from complex backgrounds and fuse multi-scale features by using a gated aggregation model (GAM). Furthermore, by analyzing the influence of eccentricity on orientation error, eccentricity-wise orientation loss (ewoLoss) is proposed to assign the penalties on the orientation loss based on the eccentricity of the RIE, which effectively improves the accuracy of the detection of oriented objects with a large aspect ratio. Extensive experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of the proposed method. |
| first_indexed | 2024-03-10T07:15:59Z |
| format | Article |
| id | doaj.art-fbb890b79f8c437684938f517d57154b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T07:15:59Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fbb890b79f8c437684938f517d57154b2023-11-22T15:05:48ZengMDPI AGRemote Sensing2072-42922021-09-011318362210.3390/rs13183622Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing ImagesXu He0Shiping Ma1Linyuan He2Le Ru3Chen Wang4Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an 710038, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an 710038, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an 710038, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an 710038, ChinaOriented object detection in remote sensing images (RSIs) is a significant yet challenging Earth Vision task, as the objects in RSIs usually emerge with complicated backgrounds, arbitrary orientations, multi-scale distributions, and dramatic aspect ratio variations. Existing oriented object detectors are mostly inherited from the anchor-based paradigm. However, the prominent performance of high-precision and real-time detection with anchor-based detectors is overshadowed by the design limitations of tediously rotated anchors. By using the simplicity and efficiency of keypoint-based detection, in this work, we extend a keypoint-based detector to the task of oriented object detection in RSIs. Specifically, we first simplify the oriented bounding box (OBB) as a center-based rotated inscribed ellipse (RIE), and then employ six parameters to represent the RIE inside each OBB: the center point position of the RIE, the offsets of the long half axis, the length of the short half axis, and an orientation label. In addition, to resolve the influence of complex backgrounds and large-scale variations, a high-resolution gated aggregation network (HRGANet) is designed to identify the targets of interest from complex backgrounds and fuse multi-scale features by using a gated aggregation model (GAM). Furthermore, by analyzing the influence of eccentricity on orientation error, eccentricity-wise orientation loss (ewoLoss) is proposed to assign the penalties on the orientation loss based on the eccentricity of the RIE, which effectively improves the accuracy of the detection of oriented objects with a large aspect ratio. Extensive experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of the proposed method.https://www.mdpi.com/2072-4292/13/18/3622oriented object detectionrotated inscribed ellipseremote sensing imageskeypoint-based detectiongated aggregationeccentricity-wise |
| spellingShingle | Xu He Shiping Ma Linyuan He Le Ru Chen Wang Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images Remote Sensing oriented object detection rotated inscribed ellipse remote sensing images keypoint-based detection gated aggregation eccentricity-wise |
| title | Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images |
| title_full | Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images |
| title_fullStr | Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images |
| title_full_unstemmed | Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images |
| title_short | Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images |
| title_sort | learning rotated inscribed ellipse for oriented object detection in remote sensing images |
| topic | oriented object detection rotated inscribed ellipse remote sensing images keypoint-based detection gated aggregation eccentricity-wise |
| url | https://www.mdpi.com/2072-4292/13/18/3622 |
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