Unsupervised Oil Spill Detection in Shipborne Radar Imagery Using Autoencoder-Enhanced Q-Learning and Improved Bat Optimization

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.