Rapid and accurate access to the oil spill information is of great significance for dynamic monitoring, conservation and sustainable use of the oceans. HJ-1 is a new satellite platform designed for ecological environmental pollutions and disasters. However, the multispectral image obtained from HJ-CCD has insufficient spectral bands, and the accuracy of acquiring the oil spill coverage only by spectral information is low. In this paper, the oil spill that occurred in the Gulf of Mexico was selected as the research object. Based on the spectral analysis of different features, the authors chose the right texture structure factors and extracted the texture characteristics which affect oil spill identification by gray co-occurrence matrix. A decision tree model combining spectral characteristics with texture characteristics was established to extract the oil spill on the sea surface. A comparative analysis by using the result of maximum likelihood supervision classification method was performed, and the results show that, in comparison with the maximum likelihood classification method, the decision tree method could improve the user's accuracy and the producer's accuracy of oil spill extraction by 11.85% and 4.28% respectively.
盖颖颖, 周斌, 孙元芳, 周燕. 基于HJ-CCD数据的海面溢油提取方法研究[J]. 国土资源遥感, 2014, 26(2): 99-104.
GAI Yingying, ZHOU Bin, SUN Yuanfang, ZHOU Yan. Study of extraction methods for ocean surface oil spill using HJ-CCD data. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 99-104.
 Camilla B,Solberg A H S.Oil spill detection by satellite remote sensing [J].Remote Sensing of Environment,2005,95(1):1-13. Carnesecchi F,Byfield V,Cipollini P,et al.An optical model for the interpretation of remotely sensed multispectral images of oil spill[C]//Charles R.Proceeding SPIE 7105,Remote Sensing of the Ocean,Sea Ice and Large Water Regions 2008.Wales,2008. Li Y,Ma L,Yu S M,et al.Remote sensing of marine oil spills and its applications[C]//Tong Q X.Remote Sensing of the Environment:16th National Symposium on Remote Sensing of China.Beijing,2007. 牛亚琴.基于遥感图像的海上溢油现象识别研究[D].舟山:浙江海洋学院,2012. Niu Y Q.Identification of marine spilled oil based on remote sensing image[D].Zhoushan:Zhejiang Ocean University,2012. Tseng W Y,Chiu L S.AVHRR observations of Persian Gulf oil spills[C]//IEEE International Geoscience and Remote Sensing Symposium.Pasadena:IEEE,1994:779-782. 侯懿峰.MODIS数据解析及海面溢油分类研究[D].大连:大连海事大学,2012. Hou Y F.The research on MODIS data processing and marine oil spill classification[D].Dalian:Dalian Maritime University,2012. 陆应诚,陈君颖,包颖,等.基于HJ-1星CCD数据的溢油遥感特性分析与信息提取[J].中国科学:信息科学,2011,41(增刊):193-201. Lu Y C,Chen J Y,Bao Y,et al.Using HJ-1 satellite CCD data for remote sensing analysis and information extraction in oil spill scenarios[J].Scientia Sinica:Informationis,2011,41(s1):193-201. 姜良美,王芳,肖志坤,等.基于纹理特征的微山湖湿地信息提取研究[J].湖南科技大学学报:自然科学版,2011,26(4):68-72. Jiang L M,Wang F,Xiao Z K,et al.Study on extracting wetland information in Weishanhu Lake area based on texture feature[J].Journal of Hunan University of Science and Technology:Natural Science Edition,2011,26(4):68-72. 张砾.辅以纹理特征的洪泽湖湿地信息提取[J].遥感信息,2010(3):30-34. Zhang L.Wetland information extraction combined with texture features[J].Remote Sensing Information,2010(3):30-34. Haralick R M,Shanmugam K,Dinstein I.Textural features for image classification[J].IEEE Transactions on Systems,Man and Cybernetics,1973,3(6):610-621. Franklin S E,Hall R J,Moskal L M,et al.Incorporating texture into classification of forest species composition from airborne multispectral images[J].International Journal of Remote Sensing,2000,21(1):61-79. Li G Y,Lu D S,Moran E,et al.A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region[J].ISPRS Journal of Photogrammetry and Remote Sensing,2012,70:26-38. 冯建辉,杨玉静.基于灰度共生矩阵提取纹理特征图像的研究[J].北京测绘,2007(3):19-22. Feng J H,Yang Y J.Study of texture images extraction based on gray level co-occurence matrix[J].Beijing Surveying and Mapping,2007(3):19-22. 谭湘莹,于秀兰,钱国蕙.一种大小窗口结合的SAR图像纹理特征分类方法[J].系统工程与电子技术,2000,22(4):15-17. Tan X Y,Yu X L,Qian G H.A classification method by use of SAR image texture characteristics with combination of large and small windows[J].Systems Engineering and Electronics,2000,22(4):15-17. Kasapoglu N G,Yazgan B,Akleman F.Hierarchical decision tree classification of SAR data with feature extraction method based on spatial variations[C]//IEEE International Geoscience and Remote Sensing Symposium.Toulouse,2003:3453-3455. 赵文吉,段福州,刘晓萌,等.ENVI遥感影像处理专题与实践[M].北京:中国环境科学出版社,2007:54. Zhao W J,Duan F Z,Liu X M,et al.ENVI processing topics and practice for remote sensing images[M].Beijing:China Environmental Science Press,2007:54. 巴桑,刘志红,张正健,等.决策树在遥感影像分类中的应用[J].高原山地气象研究,2011,31(2):31-34. Ba S,Liu Z H,Zhang Z J,et al.Decision tree and its application in remote sensing image classification[J].Plateau and Mountain Meteorology Research,2011,31(2):31-34.