Uncertainty Exploration: Toward Explainable SAR Target Detection

Deep learning-based synthetic aperture radar (SAR) target detection has been developed for years, with many advanced methods proposed to achieve higher indicators of accuracy and speed. In spite of this, the current deep detectors cannot express the reliability and interpretation in trusting the predictions, which are crucial especially for those ordinary users without much expertise in understanding SAR images. To achieve explainable SAR target detection, it is necessary to answer the following questions: how much should we trust and why cannot we trust the results. With this purpose, we explore the uncertainty for SAR target detection in this article by quantifying the model uncertainty and explaining the ignorance of the detector. First, the Bayesian deep detectors (BDDs) are constructed for uncertainty quantification, answering how much to trust the classification and localization result. Second, an occlusion-based explanation method (U-RISE) for BDD is proposed to account for the SAR scattering features that cause uncertainty or promote trustworthiness. We introduce the probability-based detection quality (PDQ) and multielement decision space for evaluation besides the traditional metrics. The experimental results show that the proposed BDD outperforms the counterpart frequentist object detector, and the output probabilistic results successfully convey the model uncertainty and contribute to more comprehensive decision-making. Furthermore, the proposed U-RISE generates an attribution map with intuitive explanations to reveal the complex scattering phenomena about which BDD is uncertain. We deem our work will facilitate explainable and trustworthy modeling in the field of SAR image understanding and increase user comprehension of model decisions.