Ship detection in SAR images using efficient land masking methods

被引:0
作者
Mashaly, Ahmed S. [1 ]
AbdElkawy, Ezz F. [1 ]
Mahmoud, Tarek A. [1 ]
机构
[1] Egyptian Armed Forces, Cairo, Egypt
来源
ALGORITHMS FOR SYNTHETIC APERTURE RADAR IMAGERY XXI | 2014年 / 9093卷
关键词
Synthetic Aperture Radar (SAR); Ship Detection System; Land Masking; Mathematical Morphology (MM);
D O I
10.1117/12.2053171
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Synthetic Aperture Radar (SAR) has an important contribution in monitoring ships in the littoral regions. This stems from the substantial information that SAR images have which can facilitate the ships detection operation. Coastline images produced by SAR suffer from many deficiencies which arise from the presence of speckles and strong signals returned from land and rough sea. The first step in many ship detection systems is to mark and reject the land in SAR images (land masking). This is performed to reduce the number of false alarms that might be introduced if the land is processed by ship detector. In this paper, two powerful methods for land masking are introduced. One is based on mathematical morphology while the other is based on Lee-Jurkevich coastline detection and mean estimator algorithm. From experimental results, the proposed methods give promising results for both strongly marking the land area in SAR images and efficiently preserving the details of coastlines as well.
引用
收藏
页数:9
相关论文
共 50 条
  • [41] Efficient Ship Detection in Synthetic Aperture Radar Images and Lateral Images using Deep Learning Techniques
    Nambiar, Athira
    Vaigandla, Ashish
    Rajendran, Suresh
    2022 OCEANS HAMPTON ROADS, 2022,
  • [42] An Improved Method of Land Masking for Synthetic Aperture Radar-based Ship Detection
    Yang, Chan-Su
    Park, Ju-Han
    Harun-Al Rashid, Ahmed
    JOURNAL OF NAVIGATION, 2018, 71 (04) : 788 - 804
  • [43] Ship Detection Based on Coherence Images Derived From Cross Correlation of Multilook SAR Images
    Ouchi, Kazuo
    Tamaki, Shinsuke
    Yaguchi, Hidenobu
    Iehara, Masato
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2004, 1 (03) : 184 - 187
  • [44] YOLO-Lite: An Efficient Lightweight Network for SAR Ship Detection
    Ren, Xiaozhen
    Bai, Yanwen
    Liu, Gang
    Zhang, Ping
    REMOTE SENSING, 2023, 15 (15)
  • [45] A Comparative Study on Methods of Improving SCR for Ship Detection in SAR Image
    Lang, Haitao
    Shi, Hongji
    Tao, Yunhong
    Ma, Li
    TARGET AND BACKGROUND SIGNATURES III, 2017, 10432
  • [46] LssDet: A Lightweight Deep Learning Detector for SAR Ship Detection in High-Resolution SAR Images
    Yan, Guoxu
    Chen, Zhihua
    Wang, Yi
    Cai, Yangwei
    Shuai, Shikang
    REMOTE SENSING, 2022, 14 (20)
  • [47] Multilayer CFAR Detection of Ship Targets in Very High Resolution SAR Images
    Hou, Biao
    Chen, Xingzhong
    Jiao, Licheng
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2015, 12 (04) : 811 - 815
  • [48] On-Board Ship Detection in SAR Images Based on L-YOLO
    Xu, Xiaowo
    Zhang, Xiaoling
    Zhang, Tianwen
    Shi, Jun
    Wei, Shunjun
    Li, Jianwei
    2022 IEEE RADAR CONFERENCE (RADARCONF'22), 2022,
  • [49] Scattering-Point-Guided RPN for Oriented Ship Detection in SAR Images
    Zhang, Yipeng
    Lu, Dongdong
    Qiu, Xiaolan
    Li, Fei
    REMOTE SENSING, 2023, 15 (05)
  • [50] A Lightweight Model for Ship Detection and Recognition in Complex-Scene SAR Images
    Xiong, Boli
    Sun, Zhongzhen
    Wang, Jin
    Leng, Xiangguang
    Ji, Kefeng
    REMOTE SENSING, 2022, 14 (23)