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In this letter, we present a new semi-empirical approach for optimized dark spot detection. The proposed method is a C-band model that identifies oil spill candidates by correlating surface wind vectors and incidence angles with SAR threshold backscatter coefficients. Specifically, our approach combines insights from wind-wave generation and radar imaging theories with the 193 global Sentinel-1 SAR oil spill data. Demonstrating a balance between accuracy and simplicity, which is crucial in dark spot detection, it achieves an F1 score of 0.8 and operates without requiring parametric adjustments or iterative processes. Its integration of wind speed data could also reduce false alarms in low-wind scenarios. These results indicate potential improvements in the efficiency of automated oil spill detection systems and their application in creating a balanced deep learning dataset.

A Semi-Empirical Threshold Model for Oil Spill Detection by Analyzing Microwave Backscatter of Ocean Surface from Sentinel-1 C-band SAR Data