LAUSR

At present, few-shot object detection research in the field of optical remote sensing images has been conducted, but few-shot object detection in the field of synthetic aperture radar (SAR) images has rarely been explored. To this end, this article proposes a lightweight metalearning-based SAR image few-shot object detection method, which improves the accuracy and speed of SAR image few-shot object detection from a more balanced perspective. First, we introduce the latest FSODM method in optical remote sensing as a benchmark framework. Second, a lightweight metafeature extractor named DarknetS is designed to enhance the feature representation of SAR images and improve detection timeliness. Furthermore, we build a new aggregation module called AggregationS, which encodes support features and query features into the same feature subspace via a novel transformer encoder. This module design can better extract the correlation and saliency between different classes in the support set, improve the detection accuracy of the query set, and enhance the detection generalization performance of new classes. Finally, we built several real-world SAR image few-shot object detection datasets to verify the effectiveness of the method. Experimental results show that FSODS can achieve a better object detection performance compared to the baseline model under the condition that only a small amount of labeled data is required for new classes of SAR image objects.