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Oil Spill Identification in Marine SAR Images Based on Texture Feature and Fuzzy Logic System

机译:基于纹理特征和模糊逻辑系统的海洋SAR图像中的漏油泄漏识别

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A model based on texture feature and fuzzy logic algorithm was constructed to discriminate oil spills from look-alike phenomena in the SAR images. Statistics texture feature of SAR images were extracted and used as the input parameters in the fuzzy logic system. The texture features consisted of entropy second order, angular second moment, contrast and inverse difference moment of dark objects. The system analyzed 38 SAR images with 77 oil spills and 52 look-alikes, and provided the probability of a dark object to be an oil spill. The remaining 26 processed SAR images, which were not included in the training, were used to test the system. The result showed that 80.5% of the oil spills were correctly classified. It seemed that the texture features and fuzzy logic system were effective in identifying oil spills on marine SAR images.
机译:构建基于纹理特征和模糊逻辑算法的模型,以区分SAR图像中的视图相似现象的油溢出。提取SAR图像的统计纹理特征,并用作模糊逻辑系统中的输入参数。纹理特征由熵的二阶,角第二矩,对比度和暗对象的反差矩组成。该系统分析了38个SAR图像,具有77个漏油和52个外观,并提供了暗对象的概率溢油。剩余的26个处理后的SAR图像未包含在培训中,用于测试系统。结果表明,80.5%的漏油机被正确分类。似乎纹理特征和模糊逻辑系统有效地识别海洋SAR图像上的漏油。

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