Synthetic aperture radar (SAR) plays an irreplaceable role in the monitoring of marine oil spills. However, due to the limitation of its imaging characteristics, it is difficult to use traditional image processing methods to effectively extract oil spill information from SAR images with coherent speckle noise. In this paper, the convolutional neural network AlexNet model is used to extract the oil spill information from SAR images by taking advantage of its features of local connection, weight sharing, and learning for image representation. The existing remote sensing images of the oil spills in recent years in China are used to build a dataset. These images are enhanced by translation and flip of the dataset, and so on and then sent to the established deep convolutional neural network for training. The prediction model is obtained through optimization methods such as Adam. During the prediction, the predicted image is cut into several blocks, and the error information is removed by corrosion expansion and Gaussian filtering after the image is spliced again. Experiments based on actual oil spill SAR datasets demonstrate the effectiveness of the modified AlexNet model compared with other approaches.

中文翻译：

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基于AlexNet模型的SAR图像溢油检测

合成孔径雷达（SAR）在海洋溢油监测中发挥着不可替代的作用。然而，由于其成像特性的限制，很难利用传统的图像处理方法从具有相干散斑噪声的SAR图像中有效提取溢油信息。本文利用卷积神经网络AlexNet模型利用其局部连接、权重共享、图像表征学习等特点，从SAR图像中提取溢油信息。利用我国近年来已有的溢油遥感影像构建数据集。这些图像通过数据集的平移和翻转等增强，然后发送到建立的深度卷积神经网络进行训练。预测模型是通过Adam等优化方法得到的。在预测过程中，将预测图像切成若干块，再次拼接图像后，通过腐蚀扩展和高斯滤波去除误差信息。与其他方法相比，基于实际溢油 SAR 数据集的实验证明了修改后的 AlexNet 模型的有效性。