FCS-Net: A Frequency-Spatial Coordinate and Strip-Augmented Network for SAR Oil Spill Segmentation
Year: 2026
Type: article
Abstract: Accurate segmentation of marine oil spills in synthetic aperture radar (SAR) images is crucial for emergency response and environmental remediation. However, current deep learning methods are still limited by two long-standing bottlenecks: first, multiplicative speckle noise and complex background clutter make it difficult to accurately delineate actual oil spills; and second, limited receptive fields often lead to the geometric fragmentation of elongated, irregular oil films. To surmount these challenges, this paper proposes a novel framework termed the Frequency-Spatial Coordinate and Strip-Augmented Network (FCS-Net). First, we leverage the ConvNeXt-Small backbone to extract robust hierarchical features, utilizing its large kernel design to capture broad contextual information. Second, a Frequency-Spatial Coordinate Attention (FS-CA) module is proposed to integrate spatial coordinate encoding with global frequency-domain information. Third, to maintain the morphological integrity of elongated targets, we introduce a Strip-Augmented Pyramid Pooling (SAPP) module which employs anisotropic strip pooling to model long-range dependencies. Extensive experiments on the multi-source SOS dataset demonstrate the effectiveness of FCS-Net. The proposed method achieves state-of-the-art performance, reaching an mIoU of 87.78% in the Gulf of Mexico and 89.62% in the challenging Persian Gulf, outperforming strong baselines and demonstrating superior robustness in complex ocean scenarios.
DOI: 10.3390/jmse14020168
Authors: Shentao Wang, Byung-Won Min, Depeng Gao, Yue Hong
Institutions: Nantong Institute of Technology, Mokwon University
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: gold
Estimated APC paid: $3,137
Funder: China University of Geosciences
OpenAlex ID: W7123346628