Modification of CFAR Algorithm for Oil Spill Detection from SAR Data

被引:11
|
作者
Wang, Siyuan [1 ]
Fu, Xingyu [2 ]
Zhao, Yan [1 ]
Wang, Hui [1 ]
机构
[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Elect, Beijing, Peoples R China
来源
关键词
CFAR; Oil spill detection; Ratio edge detection; SAR; AUTOMATIC DETECTION; NEURAL-NETWORKS; IMAGES; SEGMENTATION; PROBABILITY; INTEGRATION; SHORELINE; CLUTTER; TARGET; MODEL;
D O I
10.1080/10798587.2014.960228
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
It is very difficult to detect oil spills when the scattering intensity of background clutter is inhomogeneous in synthetic aperture radar (SAR) images. To improve the oil detection capability, we propose a modified constant false alarm rate (CFAR)-based method for the detection of oil spills in SAR images. This proposed method combines edge detection technique and CFAR detection theory to improve the accuracy of oil spills detection. First, we segment the image into the areas of interest (AOIs) by using ratio edge detection. Second, to get a more accurate detection result, an improved Weibull-CFAR detector is applied to these AOIs. Experimental results demonstrate that the modified CFAR algorithm can work more effectively than a global CFAR detector for oil spill detection, especially for the inhomogeneous intensity SAR images. This model can detect the target more effectively, and false alarms can be greatly diminished.
引用
收藏
页码:163 / 174
页数:12
相关论文
共 50 条
  • [41] Compressive sensing based CFAR target detection algorithm for SAR image
    Zhang, Y. (yuzhang.whu@gmail.com), 1600, Editorial Board of Medical Journal of Wuhan University (39):
  • [42] Marine oil spill detection and segmentation in SAR data with two steps Deep Learning framework
    Trujillo-Acatitla, Rubicel
    Tuxpan-Vargas, Jose
    Ovando-Vazquez, Cesare
    Monterrubio-Martinez, Erandi
    MARINE POLLUTION BULLETIN, 2024, 204
  • [43] Algorithms for oil spill detection in Radarsat and ENVISAT SAR images
    Solberg, AS
    Brekke, C
    Solberg, R
    IGARSS 2004: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM PROCEEDINGS, VOLS 1-7: SCIENCE FOR SOCIETY: EXPLORING AND MANAGING A CHANGING PLANET, 2004, : 4909 - 4912
  • [44] Improved two parameter CFAR ship detection algorithm in SAR images
    Ai, Jia-Qiu
    Qi, Xiang-Yang
    Yu, Wei-Dong
    Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2009, 31 (12): : 2881 - 2885
  • [45] OIL SPILL DETECTION USING SIMULATED RADARSAT CONSTELLATION MISSION COMPACT POLARIMETRIC SAR DATA
    Dabboor, Mohammed
    Singha, Suman
    Topouzelis, Konstantinos
    Flett, Dean
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 4582 - 4585
  • [46] Analysis of impacting factors on polarimetric SAR oil spill detection
    SONG Shasha
    ZHAO Chaofang
    AN Wei
    LI Xiaofeng
    WANG Chen
    Acta Oceanologica Sinica, 2018, 37 (11) : 77 - 87
  • [47] Using boosting to improve oil spill detection in SAR images
    Ramalho, Geraldo L. B.
    Medeiros, Fatima N. S.
    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS, 2006, : 1066 - +
  • [48] Optimum Features Selection for oil Spill Detection in SAR Image
    Saeed Chehresa
    Abdollah Amirkhani
    Gholam-Ali Rezairad
    Mohammad R. Mosavi
    Journal of the Indian Society of Remote Sensing, 2016, 44 : 775 - 787
  • [49] Oil Spill Detection in SAR Images Using Wavelets and Morphology
    Kuzmanic, Ivica
    Vujovic, Igor
    PROCEEDINGS ELMAR-2010, 2010, : 337 - 340
  • [50] Polarimetric SAR Oil Spill Detection based on Deep Networks
    Chen, Guandong
    Li, Yu
    Sun, Guangmin
    Zhang, Yuanzhi
    2017 IEEE INTERNATIONAL CONFERENCE ON IMAGING SYSTEMS AND TECHNIQUES (IST), 2017, : 624 - 628