基于Landsat遥感影像的海上油气平台提取与监测

doi:10.13284/j.cnki.rddl.002921

摘要/Abstract

摘要： 监测海上油气平台的开发有着巨大的经济和军事意义。针对可见光遥感影像数据源丰富的特性，提出了一种适用于Landsat系列中等分辨率的光学影像的海上油气平台的提取监测方法。首先将TM/ETM+/OLI影像进行几何校正、辐射校正、图像增强以及区域掩膜等系列预处理，其次采用多滑动窗口动态阈值遍历影像初步识别疑似油气平台，然后应用相邻时期3景影像基于像素级别的叠合判定以去除薄云以及舰船的虚警干扰，筛选出最终的油气平台，以马来西亚与文莱交界的南海海域为实验区，通过1991―2016年共21景Landsat影像，动态监测该区域近25 a来油气平台开发的历史过程，共检测66个油气平台，通过与ALOS PALSAR数据人工识别的油气平台进行对比验证，结果表明：该方法提取的检测率为82.67%，正确率达到86.11%。监测结果显示：该海域油气平台开发主要集中在20世纪90年代之前，而后近海油气新增开发平台较少，开发主要向远离陆地的深海方向扩展。

关键词: 遥感, 油气平台, 影像识别, 滑动窗口, 南海海域

Abstract: The monitoring of the development of offshore oil and gas platforms has great economic and military significance. According to the characteristics of remote sensing image data, a method for extracting and monitoring offshore oil and gas platforms was proposed based on the Landsat series optical images with medium resolution. Firstly, series pretreatments including geometric correction, radiometric correction, image enhancement, and area mask were performed on the TM/ETM+/OLI images. Secondly, images were traversed using multiple sliding windows with dynamic thresholds to recognize suspected oil and gas platforms. Finally, oil and gas platforms were screened by laminating 3 pixel-level images of consecutive periods to get rid of false alarm interference of thin clouds and the ships. In this work, the South China Sea at the junction of Malaysia and Brunei was studied with a total of 21 Landsat images from 1991 to 2016. The historical development process of the oil and gas platforms in the area in recent 25 years was dynamically monitored, and a total of 66 oil and gas platforms were detected using the proposed method. To evaluate the accuracy of the results, the oil and gas platforms in ALOS and PALSAR images were manually identified. The results showed that the platform extraction rate was 82.67% and the correction rate was 86.11%. Based on the features of highlight and invariant position of targets, the method was able to detect oil and gas platforms from complex backgrounds. It was found that distribution of oil and gas platforms could be determined by superimposing 3 images of good quality and of consecutive time series. As Landsat satellite images of a high time resolution for a long time span are available in the archives, it is possible to conduct dynamic monitoring of the historical development process of the oil and gas platforms. This effectively compensates for that SAR images cannot be used in long-term dynamic monitoring. However, as in this method, false alarm caused by clouds is mainly removed by image overlaying, when there are many clouds in the optical images, not much area can be used for monitoring, making image utilization rate low and the correction rate of oil and gas platform recognition reduced. Therefore, there are some deficiencies with the monitoring method as it is influenced by the image cloud. Images with fewer clouds should be chosen as far as possible to monitor oil and gas platforms. Monitoring results in the South China Sea at the junction of Malaysia and Brunei showed that: offshore oil and gas platforms were mainly developed in 1990s; after that, not many offshore oil and gas platforms were developed, but the deep sea far away from the land was focused on for oil and gas platform development.

Key words: remote sensing, the South China Sea, oil and gas platform, image superimposing, multi-sliding window

赵赛帅，孙超，王海江，成王玉. 基于Landsat遥感影像的海上油气平台提取与监测[J]. 热带地理, 2017, 37(1): 112-119.

ZHAO Saishuai，SUN Chao，WANG Haijiang，CHENG Wangyu. Extraction and Monitoring of Offshore Oil and Gas Platforms Based on Landsat Imagery[J]. TROPICAL GEOGRAPHY, 2017, 37(1): 112-119.

参考文献

AMOON Mehdi，BOZORGI Ahmad，Rezai-rad Gholam-ali，2013．New Method for Ship Detection in Synthetic Aperture Radar Imagery Based on the Human Visual Attention System．Journal of Applied Remote Sensing，7（1）：71599-71599．

CHATURVEDI Sudhir ，YANG ChanSu，OUCHI Kazuo ，SHANMUGAM Palanisamy，2012．Ship Recognition By Integration of SAR and AIS．Journal of Navigation，65（2）：323-337．DOI：10.1017/ S0373463311000749．

CHENG Liang，YANG Kang，TONG Lihua，LIU Yongxue，LI Manchun，2013．Invariant Triangle-based Stationary Oil Platform Detection From Multitemporal Synthetic Aperture Radar Data．Journal of Applied Remote Sensing，7（1）：302-309．

CORBANE Christina，NAJMAN Laurent，PECOUL Emilien，DEMAGISTRI Laurent，PETIT Michel，2010．A Complete Processing Chain for Ship Detection Using Optical Satellite Imagery．International Journal of Remote Sensing，31（22）：5837-5854．

冯颂斌，2007．南海争端对我国南海资源开发的影响[Ｄ]．广州：暨南大学．[FEN Songbin，2007．The impact of the South China Sea dispute on the development of South China Sea resources[Ｄ]．Guagzhou：Jinan University．]

龚小冬，李飞，张志敏，李宁，刘亚波，陈嘉琪，许致火，2015．一种新颖的高分辨率SAR舰船检测方法．雷达科学与技术，13（1）：70-75．[GONG Xiaodong，LI Fei，ZHANG Zhimin，LI Ning，LIU Yabo，CHEN Jiaqi，XU Zhihuo，2015．A Novel Ship Detection Techique in High-Resolution Sar. Radar Science and Technology，13（1）：70-75．]

黄添强，陈智文，苏立超，郑之，袁秀娟，2011．利用内容连续性的数字视频篡改检测．南京大学学报（自然科学版），47（5）：493-503．[HUANG Tianqiang，CHEN Zhiwen，SU Lichao，ZHENG Zhi，YUAN Xiujuan，2011．Digital video forgeries detection based on content continuity．Journal of Nanjing University（Natural Sciences），47（5）：493-503．]

LIU F，ZHAO F，YU W，SHI L，2012．Ship Detection and Speed Estimation Based on Azimuth Scanning Mode of Synthetic Aperture Radar．Iet Radar Sonar Navigation，6（6）：425-431．

LIU Yongxue，SUN Chao，YANG Yuhao，ZHOU Minxi，ZHAN Wenfeng，CHENG Wangyu，2016．Automatic Extraction of Offshore Platforms Using Time-series Landsat-8 Operational Land Imager Data．Remote Sensing of Environment，175：73-91．

李扬，2015．关于斐波那契多项式与切比雪夫多项式的一些恒等式．西安：西北大学．[LI Yang，2015．Some identities about the pressure Fibonacci polynomials and Chebyshev polynomials．Xi'an：Northwest University．]

林旭，洪峻，孙显，鄢懿，2014．一种基于自适应背景杂波模型的宽幅SAR图像CFAR舰船检测算法．遥感技术与应用，29（1）：75-81．[LIN Xu，HONG Jun，SUN Xian，YAN Yi，2014．New CFAR Ship Detection Algorithm Based on Adaptive Background Clutter Model in Wide Swath SAR Images．Remote Sensing Technology and Application，29（1）：75-81．]

刘颖健，唐功友，赵驯洪，2005．数学形态学方法在海面船目标检测中的应用．中国海洋大学学报（自然科学版），35（3）：511-514．[LIU Yingjian，TANG Gongyou，ZHAO Xunhong，2005．Application of Mathematical Morphology in Sea Surface Ship Detection．Periodical of Ocean Universit y of China，35（3）：511-514．]

孟若琳，邢前国，2013．基于可见光的海上船舶油井平台遥感检测．计算机应用，33（3）：708-711．[MENG Ruolin，XING Qianguo，2013．Detection of offshore ship and well platform based on optical remote sensing images．Journal of Computer Applications，33（3）：708-711．]

孙超，刘永学，李满春，陈振杰，赵赛帅，2014．近35年来热带风暴对我国南海岛礁的影响分析．国土资源遥感，26（3）：135-140．[Sun Chao，Liu Yongxue，Li Manchun，Chen Zhenjie，Zhao Saishuai，2014．An Analysis of Tropical Storms Impact on Islands andReefs in the South China Sea in the Past 35 Years．Remote Sensing for Land and Resources，26（3）：135-140．]

王加胜，刘永学，李满春，杨康，程亮，2013．基于ENVISAT ASAR的海洋钻井平台遥感检测方法——以越南东南海域为例．地理研究，32（11）：2143-2152．[WANG Jiasheng，LIU Yongxue，LI Manchun，YANG Kang，CHENG Liang，2013．Drilling Platform Detection Based on ENVISAT ASAR Remote Sensing Data：A Case of Southeastern Vietnam Offshore Area．Geographical Research，32（11）：2143-2152．]

肖国林，刘增洁，2004．南沙海域油气资源开发现状及我国对策建议．国土资源情报，（9）：1-5．[XIAO Guolin，LIU Zengjie，2004．Present situation of oil and gas resources exploitation in Nansha sea area and Countermeasures．Land and Resources Information，（9）：1-5．]

ZHU Changren，ZHOU Hui，WANG Runsheng，GUO Jun，2010．A Novel Hierarchical Method of Ship Detection From Spaceborne Optical Image Based on Shape and Texture Features．Ieee Transactions on Geoscience & Remote Sensing，48（9）：3446-3456．

张耀光，刘岩，李春平，董丽晶，2003．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，22（3）：297-304．[ZHANG Yaoguang，LIU Yan，LI Chun ping，DONG Li jing，2003．The Exploitation of Marine Oil and Gas Resources and the Tactic Countermeasures of Petroleum in China．Geographical Research，22（3）：297-304．]

赵志，2013．基于星载SAR与AIS综合的舰船目标监视关键技术研究[D]．长沙：国防科学技术大学．[ZHAO Zhi，2013．Research on the Key Technologies for Ship Surveillance by Integration of Space-borne SAR and AIS[D]．Changsha：National University of Defense Technology．]

周伟，关键，何友，2012．光学遥感图像低可观测区域舰船检测．中国图象图形学报，17（9）：1181-1187．[ZHOU Wei，GUAN Jian，HE You，2012．Ship Detection from Low Observable Regions in Optical Remote Sensing Imagery．Journal of Image and Graphics，17（9）：1181-1187．]

[1]	王艳阳, 梁宇哲, 罗伟玲, 谢贻新, 刘济坤. 基于高分遥感影像和POI的国土空间规划现状细化调查[J]. 热带地理, 2020, 40(4): 649-658.
[2]	张丽, 廖静娟, 袁鑫, 穆晓东, 宋茜茜, 毕京鹏. 1987—2017年海南岛海岸线变化特征遥感分析[J]. 热带地理, 2020, 40(4): 659-674.
[3]	周胜男, 施祺, 郭华雨, 杨红强, 严宏强. 2009—2017年南沙群岛珊瑚礁砾洲演变[J]. 热带地理, 2020, 40(4): 694-708.
[4]	丁智强, 高璇, 角媛梅, 李玉辉, 郭汝军. 基于空间密度函数的哈尼梯田世界遗产地滑坡时空格局变化[J]. 热带地理, 2020, 40(3): 478-486.
[5]	张晨, 周霞, 李勇, 杨骥, 李林. 半封闭大棚实验下广州市植被滞尘光谱特征分析[J]. 热带地理, 2020, 40(2): 266-277.
[6]	刘文祥, 舒远仲, 唐小敏, 刘金梅. 采用双注意力机制Deeplabv3+算法的遥感影像语义分割[J]. 热带地理, 2020, 40(2): 303-313.
[7]	温开祥, 李勇, 王华, 杨骥, 荆文龙, 杨传训. 基于遥感和机器学习的内陆水体水深反演技术[J]. 热带地理, 2020, 40(2): 314-322.
[8]	赵丽娴, 李长辉, 宋杨, 李熙. 基于夜光遥感影像的粤港澳地区城市发展时空格局演变[J]. 热带地理, 2020, 40(2): 243-253.
[9]	孙中宇, 荆文龙, 乔曦, 杨龙. 基于无人机遥感的盛花期薇甘菊爆发点识别与监测[J]. 热带地理, 2019, 39(4): 482-491.
[10]	刘凯, 龚辉, 曹晶晶, 朱远辉. 基于多类型无人机数据的红树林遥感分类对比[J]. 热带地理, 2019, 39(4): 492-501.
[11]	朱孟，周忠发，赵馨，黄登红，蒋翼，吴跃，崔亮. 基于无人机遥感的喀斯特高原峡谷区火龙果单株识别提取方法[J]. 热带地理, 2019, 39(4): 502-511.
[12]	张顺, 赵玉金, 白永飞, 杨龙, 孙中宇. 基于低空无人机的草原灌丛遥感辨识方法[J]. 热带地理, 2019, 39(4): 512-520.
[13]	陈黎，刘淑冰，李万能. 无人机遥感在河流岸线资源监管中的应用[J]. 热带地理, 2019, 39(4): 521-530.
[14]	孙中宇，黄钰辉，杨龙，王重洋，孙红斌，王佐霖，张卫强，甘先华. 基于无人机遥感的古银叶树群落健康快速诊断[J]. 热带地理, 2019, 39(4): 538-545.
[15]	黄登红，周忠发，吴跃，朱孟，尹林江，崔亮. 基于无人机可见光影像的高原丘陵盆地区山药植株识别[J]. 热带地理, 2019, 39(4): 571-582.