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

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.