Detection and recognition of vehicles in high-resolution SAR imagery

被引:0
|
作者
Roller, W [1 ]
Peinsipp-Byma, E [1 ]
Berger, A [1 ]
Korres, E [1 ]
机构
[1] Fraunhofer Inst Informat & Data Proc, D-76131 Karlsruhe, Germany
关键词
SAR image interpretation; sensor evaluation; image quality; interpreter experiments;
D O I
10.1117/12.436944
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Designing SAR sensors is an extremely complex process. Thereby it is very important to keep in mind the goal for which the SAR sensor has to be built. For military purpose the detection and recognition of vehicles is essential. To give recommendations for design and use of SAR sensors we carried out interpreter experiments. To assess the interpreter performance we measured performance parameters like detection rate, false alarm rate etc. The following topics were of interest: How do the SAR sensor parameters bandwidth and incidence angle influence the interpreter performance? Could the length, width and orientation of vehicles be measured in SAR-images? Which information (size, signature...) will be used by the interpreters for vehicle recognition? Using our SaLVe evaluation testbed we prepared lots of images from the experimental SAR-system DOSAR (EADS Dornier) and defined several military interpretation tasks for the trials. In a 4 weeks experiment 30 German military photo interpreters had to detect and classify tanks and trucks in X-Band images with different resolutions. To accustom the interpreters to SAR image interpretation they carried out a computer based SAR tutorial. To complete the investigations also subjective assessment of image quality was done by the interpreters.
引用
收藏
页码:142 / 152
页数:11
相关论文
共 50 条
  • [31] A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
    Seo, Dae Kyo
    Eo, Yang Dam
    APPLIED SCIENCES-BASEL, 2020, 10 (09):
  • [32] Development of Building Height Data in Peru from High-Resolution SAR Imagery
    Liu, Wen
    Yamazaki, Fumio
    Adriano, Bruno
    Mas, Erick
    Koshimura, Shunichi
    JOURNAL OF DISASTER RESEARCH, 2014, 9 (06) : 1042 - 1049
  • [33] Multi-task Learning of Sparse Autofocusing for High-Resolution SAR Imagery
    Yang Lei
    Zhang Su
    Huang Bo
    Gai Minghui
    Li Pucheng
    JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2021, 43 (09) : 2711 - 2719
  • [34] High-Resolution Tropical Cyclone Rainfall Detection From C-Band SAR Imagery With Deep Learning
    Mu, Shanshan
    Li, Xiaofeng
    Wang, Haoyu
    Zheng, Gang
    Perrie, William
    Wang, Chong
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62
  • [35] Hybrid Change Detection Based on ISFA for High-Resolution Imagery
    Xu, Junfeng
    Zhao, Chuan
    Zhang, Baoming
    Lin, Yuzhun
    Yu, Donghang
    2018 IEEE 3RD INTERNATIONAL CONFERENCE ON IMAGE, VISION AND COMPUTING (ICIVC), 2018, : 76 - 80
  • [36] A HYBRID METHOD FOR VESSEL DETECTION IN HIGH-RESOLUTION SATELLITE IMAGERY
    Karantaidis, Ioannis
    Bereta, Konstantina
    Zissis, Dimitris
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 5738 - 5741
  • [37] Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
    Wiratama, Wahyu
    Sim, Donggyu
    APPLIED SCIENCES-BASEL, 2019, 9 (07):
  • [38] Automated object recognition in high-resolution optical remote sensing imagery
    Yao, Yazhou
    Chen, Tao
    Bi, Hanbo
    Cai, Xinhao
    Pei, Gensheng
    Yang, Guoye
    Yan, Zhiyuan
    Sun, Xian
    Xu, Xing
    Zhang, Hai
    NATIONAL SCIENCE REVIEW, 2023, 10 (06)
  • [39] Orchard Water Stress Detection Using High-Resolution Imagery
    Suarez, L.
    Zarco-Tejada, P. J.
    Berni, J. A. J.
    Gonzalez-Dugo, V.
    Fereres, E.
    XXVIII INTERNATIONAL HORTICULTURAL CONGRESS ON SCIENCE AND HORTICULTURE FOR PEOPLE (IHC2010): INTERNATIONAL SYMPOSIUM ON CLIMWATER 2010: HORTICULTURAL USE OF WATER IN A CHANGING CLIMATE, 2011, 922 : 35 - 39
  • [40] Automated object recognition in high-resolution optical remote sensing imagery
    Yazhou Yao
    Tao Chen
    Hanbo Bi
    Xinhao Cai
    Gensheng Pei
    Guoye Yang
    Zhiyuan Yan
    Xian Sun
    Xing Xu
    Hai Zhang
    National Science Review, 2023, 10 (06) : 38 - 41