CWCM-Net: A Novel Feature Fusion Method for Oil Spill Detection Using Single- and Quad-Polarization SAR Data

This study proposes a feature-fusion framework that jointly takes advantage of single-polarization (SP) and quad-polarization (QP) synthetic-aperture radar (SAR) imagery for reliable detection of marine oil spills and distinguishing it from look-alike phenomena. By blending complementary polarimetric properties and textural cues at the feature level, the framework markedly reduces false-alarm rates. To facilitate fair, comparable assessments, the work introduces the Quad-Polarization Oil Spill Detection (QPOSD) dataset, a fully annotated suite of QP SAR scenes, this also alleviates the scene selection bias that has long constrained QP investigations. For demonstration, the feature-fusion method is instantiated with a lightweight channel-wise concatenated multilayer network (CWCM-Net) and trained in a parallel way on the SP and QP data. The resulting model achieves an overall accuracy of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">99.02%</b> while substantially reducing false positives, underscoring the effectiveness of the proposed feature fusion approach and the practical value of the new QPOSD dataset for large-scale, operational oil spill monitoring.