中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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
DOI10.1016/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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