Gaorui Tu

Marine oil spill accidents pose serious threats to marine ecosystems and coastal economic development. X-band marine radar provides an effective means for nearshore oil spill monitoring, yet accurate oil film segmentation remains challenging because of grayscale overlap between oil films and sea clutter, range-dependent echo attenuation, and noise interference. To address these challenges, an unsupervised oil spill detection method integrating HBA-AE ROI extraction with IAEA fine segmentation is proposed. In the coarse detection stage, an autoencoder (AE) maps pixel grayscale intensity, local mean, and local standard deviation into a compact two-dimensional latent space. The AE-derived latent representation is further optimized by HBA to enhance oil film–sea clutter separability, after which K-Means clustering identifies candidate oil spill ROIs. In the subsequent fine segmentation stage, the extracted ROI constrains the search domain, while the Improved Alpha Evolution Algorithm (IAEA) maximizes the three-class Otsu between-class variance to determine the optimal dual thresholds for oil film delineation. Experiments using X-band marine radar data collected during the Dalian 7·16 oil spill accident were conducted against seven representative methods. The proposed framework achieved a Precision of 97.20%, an F1-score of 86.60%, and an IoU of 76.40%, demonstrating superior oil film segmentation accuracy and effective false-alarm suppression under complex sea-clutter conditions. It also maintained a favorable balance between segmentation accuracy and computational efficiency, demonstrating its potential for marine oil spill monitoring under the investigated radar observation conditions.

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.

Oil slicks pose a severe threat to marine ecosystems, making accurate and real-time detection increasingly urgent. Marine X-band radar has become an essential tool for oil slick monitoring due to its high temporal resolution and its ability to sensitively capture the damping of capillary waves on the sea surface caused by oil films. Building upon this, an unsupervised and lightweight SLIC-KMeans-GJO detection framework is proposed. The method first generates superpixels by using Simple Linear Iterative Clustering (SLIC) and then applies K-means clustering to extract region of interest (ROI). An improved Golden Jackal Optimizer (GJO) is adaptively initialized based on the grayscale distribution and information entropy. To enhance optimization performance, Lévy flight and stochastic perturbation mechanisms are incorporated to improve global exploration and local convergence precision. Experimental results demonstrate that the proposed method significantly outperforms conventional thresholding approaches and other intelligent optimization-based segmentation algorithms in terms of noise suppression, target identification accuracy, and discrimination precision for oil slick targets. It effectively mitigates over-segmentation and false detections while preserving fine edge details and the true spatial extent of oil slicks. The proposed framework offers a novel and practical solution for real-time oil slick monitoring, holding strong potential for operational maritime emergency response.