An Enhanced Probabilistic Posterior Sampling Approach for Synthesizing SAR Imagery With Sea Ice and Oil Spills

Although the synthesis of the synthetic aperture radar (SAR) imagery with both sea ice and oil spills can significantly benefit in improving the consistency and comprehensiveness of testing and evaluating algorithms that are designed for mapping cold ocean regions, creating such imagery is difficult due to the heterogeneity and complexity of the source images. This letter presents an enhanced region-based probabilistic posterior sampling approach to effectively synthesize SAR imagery with different ocean features. In the proposed approach, instead of relying entirely on the SAR intensity values, the posterior sampling is performed based on a number of quantitative factors, such as intensity, label field, and the prior class probability of sampling candidates, constituting a complete probabilistic framework that addresses key aspects in the synthesis of SAR imagery from heterogeneous sources. The experiments demonstrate that the proposed approach can better address the difficulties caused by the heterogeneity in the source images compared with the existing state-of-the-art ice synthesis method, and it will improve the consistency, comprehensiveness, and fairness of the evaluation of the remote sensing classification and segmentation algorithms.