Application of multi-resolution techniques, intelligent classification and semantic data fusion for the identification of oil spills in SAR imagery.

This paper addresses the problem of automatic oil spill detection in Synthetic Aperture Radar (SAR) imagery, a critical capability for maritime monitoring and disaster risk reduction. Despite extensive research, reliable discrimination between oil spills and look-alike phenomena remains a major challenge, particularly under heterogeneous sea-state conditions and limited availability of labelled data. To overcome these limitations, this work proposes an end-to-end processing framework that integrates multi-resolution candidate detection, adaptive pixel-level segmentation, feature-based classification and contextual data fusion within a unified pipeline. The proposed approach introduces a multi-scale statistical modelling strategy for sea clutter characterization, enabling robust detection of heterogeneous regions while preserving sensitivity to potential oil slicks. A compact and discriminative feature space combining morphological and radiometric descriptors is optimized for Support Vector Machine (SVM) classification under limited training data conditions. A key contribution of this work is the incorporation of external semantic information, such as wind fields and potential pollution sources, as a reliability enhancement layer, bridging the gap between image-based detection and operational decision-making. The methodology is validated using SAR imagery acquired by PAZ and TerraSAR-X sensors over multiple maritime scenarios, including real pollution events. Results demonstrate that the proposed framework achieves a favourable trade-off between detection sensitivity and false alarm reduction, outperforming representative segmentation approaches while maintaining accurate delineation of oil slicks. The classification stage reaches an accuracy of up to 96.8% using a reduced feature set. Overall, the results confirm the potential of the proposed framework for robust, automatic oil spill identification in operational scenarios.