基于纹理分析和人工神经网络的SAR图像中海面溢油识别方法  

Oil Spill Identification in Marine SAR Images Based on Texture Feature and Artificial Neural Network

作　　者：石立坚[1,2] 赵朝方[1] 刘朋[1] 

机构地区：[1]中国海洋大学海洋遥感研究所,海洋遥感教育部重点实验室,山东青岛266003 [2]国家卫星海洋应用中心,北京100081

出　　处：《中国海洋大学学报（自然科学版）》2009年第6期1269-1274,1314,共7页Periodical of Ocean University of China

基　　金：国家高技术研究发展计划项目(2006AA06Z415);国家科技支撑计划“应急空间数据中心系统”(2008BAK52B05)资助

摘　　要：基于纹理分析和人工神经网络建立了用于区别SAR图像中溢油现象和疑似溢油现象的模型。引入图像处理中的纹理分析作为识别溢油现象的特征参量,并利用方差分析对计算的31个特征参量进行筛选作为神经网络的输入。结果表明,模型能够较好的识别溢油现象,测试样本集的总体精度为0.83;纹理特征作为特征参量以及基于方差分析的特征参量筛选提高了溢油现象的识别精度。A model based on texture feature and Artificial Neural Network（ANN） was constructed to distinguish oil spills and look-like phenomena in the SAR images. Statistic texture features of SAR images were extracted, in addition to the grey features, to be used as the inputs of ANN. The analysis of variance（ANOVA） was used to evaluate the importance of the features in distinguishing oil spills from the look-likes phenomena. The selected 16 features were used as the input of ANN. We found that 83 % of the total test data were classified correctly, and it seems that the second-order statistic features based on co-occurrence matrix and features filtering with ANOVA improve the result of oil spills identification compared with the results of other methods.

关 键 词：合成孔径雷达(SAR) 溢油识别 纹理特征 神经网络 方差分析 

分 类 号：TE135.1[石油与天然气工程—油气勘探]