Construction and Application of Marine Oil Spill Gravity Vector Differences Detection Model

被引:0
作者
Su, Weiguang [1 ,2 ,4 ]
Ping, Bo [3 ]
Su, Fenzhen [1 ]
机构
[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, LREIS, Beijing, Peoples R China
[2] Chinese Acad Sci, YICCAS,Yantai Inst Coastal Zone Res YIC, Key Lab Coastal Zone Environm Proc, Shandong Prov Key Lab Coastal Zone Environm Proc, Yantai, Shandong, Peoples R China
[3] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan, Peoples R China
[4] Univ Chinese Acad Sci, Beijing, Peoples R China
来源
IMAGE ANALYSIS AND PROCESSING (ICIAP 2013), PT II | 2013年 / 8157卷
基金
国家高技术研究发展计划(863计划);
关键词
marine oil spill; gravity; vector differences; SAR IMAGES; AUTOMATIC DETECTION; SEGMENTATION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes a new marine oil spill gravity vector differences detection model based on scalability or viscosity of the oil and water. The model used the median filtering, zero pixels elimination, image normalization, nonlinear transformation, and brought in the law of gravity. The research was upon two oil spill incidents which occurred on the Mediterranean Sea in 2004 and the Gulf of Mexico in 2006. Based on the MODIS remote sensing data, we executed the model to detect the two incidents and compared the results with the results of Sobel detection algorithm. The experimental results illustrated that the model introduced in this paper is superior to Sobel detection algorithm. The proposed model is powerful in oil spill detection.
引用
收藏
页码:703 / 710
页数:8
相关论文
共 21 条
[21]   A Semi-Empirical Threshold Model for Oil Spill Detection by Analyzing Microwave Backscatter of Ocean Surface From Sentinel-1 C-Band SAR Data [J].
Park, Soyeon ;
Li, Chenglei ;
Kim, Hwisong ;
Kim, Duk-jin .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2024, 21 :1-5