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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Mapping Oil Spills from Dual-Polarized SAR Images Using an Artificial Neural Network: Application to Oil Spill in the Kerch Strait in November 2007
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Mapping Oil Spills from Dual-Polarized SAR Images Using an Artificial Neural Network: Application to Oil Spill in the Kerch Strait in November 2007

机译：使用人工神经网络绘制双极化SAR图像中的溢油图：2007年11月在刻赤海峡溢油中的应用

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摘要

Synthetic aperture radar (SAR) has been widely used to detect oil-spill areas through the backscattering intensity difference between oil and background pixels. However, since the signal is similar to that produced by other phenomena, positive identification can be challenging. In this study we developed an algorithm to effectively analyze large-scale oil spill areas in SAR images by focusing on optimizing the input layer to artificial neural network (ANN) through removal the factor of lowering the accuracy. An ANN algorithm was used to generate probability maps of oil spills. Highly accurate pixel-based data processing was conducted through false or un-detection element reduction by normalizing the image or applying a non-local (NL) means filter and median filter to the input neurons for ANN. In addition, the standard deviation of co-polarized phase difference (CPD) was used to reduce false detection from the look-alike with weak damping effect. The algorithm was validated using TerraSAR-X images of an oil spill caused by stranded oil tanker Volganefti-139 in the Kerch Strait in 2007. According to the validation results of the receiver operating characteristic (ROC) curve, the oil spill was detected with an accuracy of about 95.19% and un-detection or false detection by look-alike and speckle noise was greatly reduced.
机译：合成孔径雷达（SAR）已被广泛用于通过油和背景像素之间的反向散射强度差来检测溢油区域。但是，由于该信号与其他现象所产生的信号相似，因此进行积极的识别可能具有挑战性。在这项研究中，我们着重于通过消除降低精度的因素来优化人工神经网络（ANN）的输入层，从而开发了一种可有效分析SAR图像中大规模漏油区域的算法。使用ANN算法生成溢油概率图。通过对图像进行归一化或对输入神经元应用非局部（NL）均值滤波器和中值滤波器，通过对虚假或未检测到的元素进行归类来进行高精度的基于像素的数据处理。此外，使用同极化相位差（CPD）的标准偏差来减少具有弱阻尼效果的外观的错误检测。使用2007年刻赤海峡搁浅的油轮Volganefti-139造成的溢油的TerraSAR-X图像验证了该算法。根据接收器工作特性（ROC）曲线的验证结果，使用准确度约为95.19％，极大地减少了因外观和斑点噪声而导致的未检测到或错误检测。

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