Navigating the Shadows: A Comparative Analysis of SAR and Optical Imagery for Detecting (Dark) Vessels - Mourampetzis,Athanasios Emmanouil (Master Thesis in Geographical Information Science, 2025)
Introduction Illicit maritime activities frequently involve "dark" vessels that disable or manipulate their Automatic Identification System (AIS) signals to evade detection. This study evaluates the effectiveness of Synthetic Aperture Radar (SAR) and Optical imagery for vessel detection using both “manual” and automated Deep Learning (DL) methods while integrating AIS data to validate, improve detection accuracy and gain insights into vessel activities. Methods Using the Gulf of Lakonia, Greece, as a case study the research examines the capabilities of a SAR based Constant False Alarm Rate (CFAR) method, a SAR pre-trained detection model and an Optical imagery based Mask R-CNN Deep Learning (DL) approach for detecting vessels. The results are combined with AIS data in order to verify the satellite detections and potentially gain insights as to the vessels activities. Key Results The SAR CFAR method, relying on manual pre-processing and a calibrated CFAR detection algorithm, demonstrated a near perfect accuracy (F1 score: 100%) in detecting vessels, proving to be unaffected by factors like cloud cover. Conversely, the SAR pre-trained model exhibited a projection offset and lower detection accuracy (F1 Score: 67%) due to a projection offset, limiting its applicability and AIS integration. The Optical imagery based DL model achieved an F1 score of 90% with limitations arising from training dataset diversity, land/sea mask quality and cloud cover. Despite these challenges, Optical imagery provided valuable descriptive insights including vessel structure and color, which facilitated vessel identification (correlate imagery detection with a vessel’s name via the AIS). The integration of SAR and Optical imagery with AIS data enabled the detection of AIS manipulation and uncovered dark vessels engaged in Ship-To-Ship (STS) transfers. However, erroneous AIS data highlighted the necessity of multi-source approaches for result validation. This study underscores the complementary nature of SAR and Optical imagery in maritime surveillance and highlights key challenges including the need for scalable automated solutions to improve detection accuracy and reduce reliance on manual processing. Methodological Insights and Future Directions The research demonstrates that integrating SAR and Optical imagery with AIS data can validate the detection results as well as provide vessel identification capabilities. While SAR imagery excels in detecting vessels, Optical imagery offers visual detail that aid vessel identification and classification. The study's findings look for further refinement of deep learning models, improved training datasets and enhanced integration techniques to minimize dependency on manual processes and account for erroneous data. Future research should focus on automating validation methods and develop Artificial Intelligence frameworks in scalable solutions. These have the possibility to assist authorities in strengthening their efforts to detect and deter illicit maritime activities.
Keywords: Geography; GIS; Synthetic Aperture Radar (SAR); Constant False Alarm Rate (CFAR); Optical Imagery; Automatic Identification System (AIS); Mask Regional Convolutional Network (Mask R-CNN); Maritime Surveillance; Dark Vessels