【摘要】 海上溢油事故的发生不仅给人类造成了巨大的财产经济损失，而且严重破坏了海洋生态环境。极化合成孔径雷达（PolSAR）通过利用多种极化通道能够更综合地记录地物后向散射信息，从而广泛应用于海上溢油检测中。为了更加准确地进行海上溢油检测，提出一种基于Dual Encoder-Decoder Net(Dual-EndNet)的极化SAR海上溢油检测算法。首先提取出目前常用的30种用于溢油检测的极化特征，并利用随机森林算法选出区分溢油重要性较好的前10个特征；然后以编码器-解码器为基本框架，设计两个分支，分别输入PauliRGB图像和优选的10个极化特征图像，用于提取溢油极化SAR图像的空间信息和极化信息，进而对两分支信息进行融合，以提高溢油检测算法性能。在两景Radarsat-2全极化SAR溢油数据集上的实验结果表明，所提方法不仅具有较强的溢油检测能力，而且能够有效地区分原油、植物油、乳化油不同类型的油膜。更多还原

【Abstract】 Marine oil spill accidents not only result in huge property and economic losses but also adversely affect the marine ecosystem. The polarimetric synthetic aperture radar(PolSAR) is widely used for marine oil spill detection because it can record the backscattering information of ground objects comprehensively through various polarization channels. To detect offshore oil spills more accurately, this study proposes a PolSAR marine oil spill detection algorithm based on a Dual Encoder-Decoder Net(Dual-EndNet). First, the 30 polarimetric features commonly used for oil spill detection were extracted from the data, and the top 10 features with high importance for oil spill detection were selected by a random forest algorithm. Next, using the encoder-decoder as the basic framework, the two branches were designed to input the PauliRGB images and the selected 10 polarimetric feature images, respectively. These were used to extract the spatial information and polarization information from the PolSAR images of the oil spill. Then, the two branches of information are merged to improve the network performance. Experiments conducted on two Radarsat-2 fully PolSAR oil spill datasets show that the proposed method has a strong oil spill detection capability, and can effectively distinguish different types of oil films, including mineral oil, biogenic film, and emulsions.更多还原