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.