Department of Geoinformatics, University of Seoul, Seoul 02504, Korea.
Sensors (Basel). 2018 Jul 11;18(7):2237. doi: 10.3390/s18072237.
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.
合成孔径雷达 (SAR) 已广泛用于通过油和背景像素之间的反向散射强度差异来检测溢油区域。然而，由于信号类似于其他现象产生的信号，因此难以进行正面识别。在这项研究中，我们开发了一种算法，通过专注于通过去除降低准确性的因素来优化人工神经网络 (ANN) 的输入层，从而有效地分析 SAR 图像中的大规模溢油区域。ANN 算法用于生成溢油概率图。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，对基于像素的高精度数据进行处理，以减少来自具有弱阻尼效果的相似物的误检或漏检。该算法使用 TerraSAR-X 图像对 2007 年在刻赤海峡搁浅油轮 Volganefti-139 造成的溢油进行了验证。根据接收者操作特性 (ROC) 曲线的验证结果，溢油的检测准确率约为 95.19%，并大大减少了相似物和斑点噪声引起的漏检或误检。
