Gallego Antonio-Javier, Gil Pablo, Pertusa Antonio, Fisher Robert B
Pattern Recognition and Artificial Intelligence Group, Department of Software and Computing Systems, University of Alicante, E-03690 Alicante, Spain.
Computer Science Research Institute, University of Alicante, E-03690 Alicante, Spain.
Sensors (Basel). 2018 Mar 6;18(3):797. doi: 10.3390/s18030797.
In this work, we use deep neural autoencoders to segment oil spills from Side-Looking Airborne Radar (SLAR) imagery. Synthetic Aperture Radar (SAR) has been much exploited for ocean surface monitoring, especially for oil pollution detection, but few approaches in the literature use SLAR. Our sensor consists of two SAR antennas mounted on an aircraft, enabling a quicker response than satellite sensors for emergency services when an oil spill occurs. Experiments on TERMA radar were carried out to detect oil spills on Spanish coasts using deep selectional autoencoders and RED-nets (very deep Residual Encoder-Decoder Networks). Different configurations of these networks were evaluated and the best topology significantly outperformed previous approaches, correctly detecting 100% of the spills and obtaining an F 1 score of 93.01% at the pixel level. The proposed autoencoders perform accurately in SLAR imagery that has artifacts and noise caused by the aircraft maneuvers, in different weather conditions and with the presence of look-alikes due to natural phenomena such as shoals of fish and seaweed.
在这项工作中，我们使用深度神经自动编码器从侧视机载雷达（SLAR）图像中分割出石油泄漏区域。合成孔径雷达（SAR）已被广泛用于海洋表面监测，特别是用于石油污染检测，但文献中很少有方法使用SLAR。我们的传感器由安装在飞机上的两个SAR天线组成，在发生石油泄漏时，能够比卫星传感器更快地响应应急服务。利用深度选择自动编码器和RED网络（非常深的残差编码器-解码器网络）对TERMA雷达进行了实验，以检测西班牙海岸的石油泄漏。对这些网络的不同配置进行了评估，最佳拓扑结构明显优于以前的方法，在像素级别上正确检测出了100%的泄漏区域，F1分数达到了93.01%。所提出的自动编码器在存在由飞机机动引起的伪影和噪声、不同天气条件以及由于鱼群和海藻等自然现象而存在相似物的SLAR图像中表现准确。
