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.

Marine oil spill accidents pose a serious threat to the marine ecological environment. Therefore, efficient and accurate oil spill detection is of great significance for emergency response. To address the issues of blurred oil-slick boundaries, prominent co-frequency interference and severe speckle noise in shipborne radar images, this study proposed an oil spill detection method based on radar data collected from a real oil spill event at a terminal in Dalian Bay. The proposed method integrates an autoencoder, feature dimensionality reduction, pseudo-labeling, reinforcement learning and an improved intelligent optimization algorithm. First, an autoencoder was adopted to extract compact nonlinear local features from the radar images, and principal component analysis (PCA) was employed for feature dimensionality reduction. Subsequently, K-Means clustering was used to construct pseudo-labels, and the reduced features were discretized to build the state space for reinforcement learning. Based on this, the Q-learning algorithm was introduced to automatically extract the region of interest (ROI). Finally, for the ROI, an improved bat algorithm incorporating a dynamic weighting factor and a multi-constraint fitness function was designed to achieve fine segmentation of the oil-slick target. The experimental results showed that the proposed method outperformed classic intelligent optimization algorithms and the conventional bat optimization algorithm in oil-slick segmentation performance. Ablation experiments further verified the effectiveness of autoencoder-based feature learning, K-Means pseudo-labeling, and Q-learning-based ROI localization. This method may provide a new technical approach for timely offshore oil spill monitoring and emergency analysis.

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.