Detection of ships in inland river using high-resolution optical satellite imagery based on mixture of deformable part models
文献类型:期刊论文
作者 | Song, Pengfei1,2,3; Qi, Lei1,4; Qian, Xueming2; Lu, Xiaoqiang1 |
刊名 | Journal of Parallel and Distributed Computing |
出版日期 | 2019-10 |
卷号 | 132页码:1-7 |
ISSN号 | 07437315 |
关键词 | Inland river Ship detection Optical satellite imagery Deformable part model |
DOI | 10.1016/j.jpdc.2019.04.013 |
产权排序 | 1 |
英文摘要 | Ship detection using optical satellite imagery is of great significance in many applications such as traffic surveillance, pollution monitoring, etc. So far, a lot of ship detection methods have been developed for images covering open sea, offshore area and harbors. Compared to the ship detection in sea and offshore area, it is more difficult to detect ships in inland river due to several challenges. First of all, many ships in inland river are clustered together and hard to be separated from each other. Secondly, ships lying alongside the pier are very likely to be recognized as part of the pier. Thirdly, ships in inland river is usually smaller than those in the sea. A hierarchical method is proposed to detect the ships in inland river in this paper. The Regions of Interest (ROIs) are firstly extracted based on water–land segmentation using multi-spectral information. Then two kinds of ship candidates are extracted based on the panchromatic band. The isolated ships are detected by analyzing the shape of connected components and the clustered ships are detected by using mixtures multi-scale Deformable Part Models (DPM) and Histogram of Oriented Gradient (HOG). At last, a Back Propagation Neural Network (BPNN) is trained to classify the ship candidates using the multi-spectral bands. The experiments using Quickbird satellite images show that our approach is effective in ship detection and performs particularly well in separating the ships clustered together and staying alongside the pier. © 2019 Elsevier Inc. |
语种 | 英语 |
出版者 | Academic Press Inc. |
WOS记录号 | WOS:000476580400001 |
源URL | [http://ir.opt.ac.cn/handle/181661/31540] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Lu, Xiaoqiang |
作者单位 | 1.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Science, Xi'an, Shaanxi; 710119, China; 2.Xi'an Jiaotong University, Xi'an, 710049, China; 3.CCCC Railway Consultants Group Company Limited, Beijing; 100088, China; 4.University of Chinese Academy of Sciences, Beijing; 100049, China |
推荐引用方式 GB/T 7714 | Song, Pengfei,Qi, Lei,Qian, Xueming,et al. Detection of ships in inland river using high-resolution optical satellite imagery based on mixture of deformable part models[J]. Journal of Parallel and Distributed Computing,2019,132:1-7. |
APA | Song, Pengfei,Qi, Lei,Qian, Xueming,&Lu, Xiaoqiang.(2019).Detection of ships in inland river using high-resolution optical satellite imagery based on mixture of deformable part models.Journal of Parallel and Distributed Computing,132,1-7. |
MLA | Song, Pengfei,et al."Detection of ships in inland river using high-resolution optical satellite imagery based on mixture of deformable part models".Journal of Parallel and Distributed Computing 132(2019):1-7. |
入库方式: OAI收割
来源:西安光学精密机械研究所
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