LAUSR

In recent years, object detection in high-resolution synthetic aperture radar (SAR) images has made significant progress, especially after the introduction of deep learning. However, objects such as dense oil tanks, which are compactly arranged in SAR images, are still challenging to recognize due to the unique imaging mechanism of SAR. Inspired by human learning from comparison, we propose a multistage framework for oil tank detection in SAR images using optical image enhancement. Specifically, in the training stage, we build a teacher–student network to align the semantic information between the two modalities, where the optical features are used to guide the corresponding SAR feature learning, while in the inference stage, the learned network detects oil tanks using only SAR images as input. Besides, a pretraining stage before training is applied to further improve the network’s ability for SAR feature extraction, which is realized by the proposed paired optical-SAR self-supervised learning. To verify the effectiveness of the proposed method, we perform experiments on our newly built SpaceNet6-OTD dataset. Extensive experiments demonstrate that the proposed method can effectively improve the accuracy of detecting oil tanks in SAR images. Datasets, codes, and more results will be released at: https://EIS-VIPG.github.io/SpaceNet6-OTD/.