Detecting Maneuvering Target Accurately Based on a Two-Phase Approach From Remote Sensing Imagery
文献类型：期刊论文
| 作者 | Chen, Xueyun1; Lin, Jingyu1; Xiang, Shiming2 ; Pan, Chun-Hong2
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| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2020-05-01 |
| 卷号 | 17期号:5页码:849-853 |
| 关键词 | Databases Testing Proposals Object detection Kernel Satellites Gray-scale Maneuvering target detection remote sensing spatial density building net (SDBN) |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2019.2935230 |
| 通讯作者 | Chen, Xueyun(cxy177@163.com) |
| 英文摘要 | Maneuvering target detection in satellite images is difficult due to their small sizes, blurred appearances under various illuminations and shadows, and occlusion by trees and buildings. Recently, a fully convolutional regression network (FCRN) was proposed and achieved by the state-of-the-art performance in the Munich vehicle database. However, such a one-phase approach often makes mistakes at difficult places because of its swift glance and rejecting any second check. In this letter, a new object spatial density building net (SDBN) was designed, and a two-phase detection approach was proposed. It used the first SDBN to generate candidate regions and the second SDBN to proceed with a meticulous check on the object categories. Experiments on four maneuvering target databases, the Munich vehicle database, the Open Vehicle Database of San-Francisco (OVDS), the Overhead Imagery Research Data Set (OIRDS), and the Open Aircraft Database (OAD) show that the proposed method outperforms FCRN by an obvious margin. In addition, the accurate geometrical parameters (positions, orientations, and lengths) of all the objects were computed based on the spatial density maps, and the published experimental result of FCRN in OIRDS was pointed out and the corrected result was given. All source codes and databases are available at http://www.github.com/cxy177/SDBN. |
| WOS关键词 | VEHICLE DETECTION |
| 资助项目 | National Natural Science Foundation of China[61661006] ; National Natural Science Foundation of China[61561005] ; National Natural Science Foundation of China[91646207] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000529957500025 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/39385]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 通讯作者 | Chen, Xueyun |
| 作者单位 | 1.Guangxi Univ, Coll Elect Engn, Nanning 530004, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Xueyun,Lin, Jingyu,Xiang, Shiming,et al. Detecting Maneuvering Target Accurately Based on a Two-Phase Approach From Remote Sensing Imagery[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2020,17(5):849-853. |
| APA | Chen, Xueyun,Lin, Jingyu,Xiang, Shiming,&Pan, Chun-Hong.(2020).Detecting Maneuvering Target Accurately Based on a Two-Phase Approach From Remote Sensing Imagery.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,17(5),849-853. |
| MLA | Chen, Xueyun,et al."Detecting Maneuvering Target Accurately Based on a Two-Phase Approach From Remote Sensing Imagery".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 17.5(2020):849-853. |
入库方式： OAI收割
来源：自动化研究所
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