Marine Oil Film Segmentation Based on GCN-IGJO Method

Marine oil spills pose significant threats to ecosystems and coastal economies, making accurate oil film detection from remote sensing data a critical task. This study proposes a two-stage method, GCN-IGJO, for segmenting oil films in challenging X-band radar images. The method first uses a Graph Convolutional Network (GCN) to learn high-order features from pixel data, enabling effective initial region extraction. Subsequently, an Improved Golden Jackal Optimization (IGJO) algorithm is introduced to perform precise threshold segmentation, incorporating specialized strategies to bias the search towards the low-intensity values characteristic of oil slicks. Experimental comparisons demonstrate that the proposed GCN-IGJO method achieves a superior balance between high precision and good recall, outperforming several baseline and alternative methods. The results validate the effectiveness of combining deep graph learning with an enhanced metaheuristic optimizer for the accurate segmentation of weak-target oil films.