The positional information of offshore drilling platform is very important to oil spill monitoring and shipping routes safety. However, confronted with the difficulty of extraction and verification, nowadays few studies have focused on offshore drilling platform detection. This paper presents an offshore drilling platform extraction method based on the constant false alarm rate (CFAR) detection algorithm according to the movement characteristics between ships and drilling platforms. There are three main steps in the method: a) Generate a land mask of the study area based on the GDEM data; b) Conduct offshore objects detection based on dual-parameter CFAR algorithm by using two temporally-adjacent ENVISAT ASAR images; c) Remove false ship alarms and configure drilling platforms according to the positional differences of objects between two phases. The southeastern Vietnam offshore area was selected as an experimental area to examine the presented method. The results demonstrate the effectiveness of CFAR method for drilling platform targets extraction. In this study area, 30 drilling platforms were extracted, which are mainly located in the Bach Ho and Rong oil fields in the Vietnam oil block 09-1.
WANG Jiasheng, LIU Yongxue, LI Manchun, YANG Kang, CHENG Liang
. Drilling platform detection based on ENVISAT ASAR remote sensing data：A case of southeastern Vietnam offshore area[J]. GEOGRAPHICAL RESEARCH, 2013
, 32(11)
: 2143
-2152
.
DOI: 10.11821/dlyj201311015
[1] 张耀光, 刘岩, 李春平, 等. 中国海洋油气资源开发与国家石油安全战略对策. 地理研究, 2003, 22(3): 297-304.
[2] Graziano M D, D'errico M, Razzano E. Constellation analysis of an integrated AIS/remote sensing spaceborne system for ship detection. Advances in Space Research, 2012, 50(3): 351-362.
[3] Atteia G, Collins M J. On the use of compact polarimetry SAR for ship detection. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 80: 1-9.
[4] Wu G, Leeuw J D, Skidmore A K, et al. Performance of Landsat TM in ship detection in turbid waters. International Journal of Applied Earth Observations and Geoinformation, 2008, 11(1): 54-61.
[5] 唐沐恩, 林挺强, 文贡坚. 遥感图像中舰船检测方法综述. 计算机应用研究, 2011, 28(1): 29-36.
[6] Yang J, Gao W. New method for ship detection. In: 2012 IEEE International. Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2012. 115-118.
[7] Mirghasemi S, Yazdi H S, Lotfizad M. A target-based color space for sea target detection. Applied Intelligence, 2012, 36(4): 960-978.
[8] Chaturvedi S K, Yang C, Ouchi K, et al. Ship recognition by integration of SAR and AIS. Journal of Navigation, 2012, 65(2): 323-337.
[9] Liu F, Zhao F, Yu W, et al. Ship detection and speed estimation based on azimuth scanning mode of synthetic aperture RADAR. IET Radar Sonar and Navigation, 2012, 6(6): 425-431.
[10] 王保云, 张荣, 袁圆, 等. 可见光遥感图像中舰船目标检测的多阶阈值分割方法. 中国科学技术大学学报, 2011, 41 (4): 293-298.
[11] 储昭亮, 王庆华, 陈海林, 等. 基于极小误差阈值分割的舰船自动检测方法. 计算机工程, 2007, 33(11): 239-241.
[12] 艾加秋, 齐向阳, 禹卫东. 改进的SAR图像双参数CFAR舰船检测算法. 电子与信息学报, 2009, 31(12): 2881-2885.
[13] 魏中平. 基于CFAR的SAR目标检测. 电子科技, 2013, 26(3): 26-29.
[14] 种劲松, 朱敏慧. 高分辨率合成孔径雷达图像舰船检测方法. 测试技术学报, 2003, 17(1): 15-18.
[15] 张东晓, 何四华, 杨绍清. 一种多尺度分形的舰船目标检测方法. 激光与红外, 2009, 39(3): 315-318.
[16] 李晓玮, 种劲松. 基于小波分解的K-分布SAR图像舰船检测. 测试技术学报, 2007, 21(4): 350-354.
[17] 田明辉, 万寿红, 岳丽华. 遥感图像中复杂海面背景下的海上舰船检测. 小型微型计算机系统, 2008, 29(11): 2162-2166.
[18] Xiangwei X, Kefeng J, Huanxin Z, et al. A fast ship detection algorithm in SAR imagery for wide area ocean surveillance. In: 2012 IEEE. Radar Conference (RADAR). IEEE, 2012. 570-574.
[19] 孟若琳, 邢前国. 基于可见光的海上船舶油井平台遥感检测. 计算机应用, 2013, 31(3): 708-711.
[20] 王定亚, 丁莉萍. 海洋钻井平台技术现状与发展趋势. 石油机械, 2010, 38(4): 69-72.
[21] Wackerman C C, Friedman K S, Pichel W G, et al. Automatic detection of ships in RADARSAT-1 SAR imagery. Canadian Journal of Remote Sensing, 2001, 27(4): 371-378.
[22] 高永年, 张万昌. 遥感影像地形校正研究进展及其比较实验. 地理研究, 2008, 27(2): 467-477.
[23] 李颖, 李传龙, 王俊, 等. ASAR level 1B数据格式及解译方法. 遥感信息, 2009, (3): 73-77.
