SAROD: EFFICIENT END-TO-END OBJECT DETECTION ON SAR IMAGES WITH REINFORCEMENT LEARNING

Generally, object detection on Synthetic-Aperture Radar (SAR) images is known to be more challenging than that in Electro-Optical (EO) satellite images because SAR images have non-negligible speckle noise and require extensive data pre-processing. Nevertheless, object detection in SAR images is important, as SAR imagery can be obtained under severe weather and time conditions. While many recent object detection approaches on SAR imagery focus on improving detection accuracy, few studies focus on improving processing efficiency. In fact, there are significant challenges and trade-offs to achieve both high accuracy and efficiency at the same time. In this work, we introduce SAROD, a novel efficient end-to-end object detection framework on SAR images based on Reinforcement Learning (RL) to balance the tradeoffs. Our proposed model consists of two detectors, coarse and fine-grained detectors, with an RL agent, where RL has not yet been utilized for object detection on SAR imagery. Our model was evaluated on a challenging SAR imagery dataset, achieving performance comparable to state-of-the-art detectors while maintaining high efficiency of source data usage.