Marine Oil Spill Classification Based on the Concatenation of Single and Quad-Polarimetric Sar Features

The expansion of marine transportation, along with the growth of offshore oil exploration and extraction, has elevated the risk of marine oil spill incidents which represents a serious threat to the marine environment. Polarimetric SAR (PolSAR) has proven its advantage in distinguishing clean seawater, mineral oil spills, and its biogenic look-alikes. However, the number of quad polarimetric Multi-Look Complex (MLC) UAVSAR images with verified marine oil spills is very limited. Meanwhile, plenty of SAR oil spill sample images are in the single polarimetric format. Therefore, it is a promising approach to jointly use the semantic information provided by single-pol SAR dataset and the polarimetric backscattering information provided by the quad-pol dataset to improve the accuracy of oil spill detection. In response, this study proposes a novel study of feature concatenation (features fusion) technique to integrate information from two of these different sources. It combines the capabilities of the U-Net model, which processes single-pol SAR images, with those of a 9-channels RV (Real Valued) U-Net model, which handles quad-pol images. The experiment preliminarily proves the effectiveness of the proposed method.