Abstract: The high-precision datum semantic information obtained by aerial remote sensing target detection technology can effectively improve the perception dimension of scene matching navigation system.Due to the large scale,high target density and high annotation cost of remote sensing images,the training and application of high-performance detection models are limited.In order to solve the problems of difference in data distribution between source domain,target domain and insufficient annotation data in target domain in semantic object detection in scene matching scenes,an efficient active learning method is proposed to optimize the selection of target domain samples.This method uses the labeled data in the source domain and the unlabeled data in the target domain to select high-information samples from the target domain for manual labeling through the active learning strategy,so as to make up for the impact of insufficient data labeling in the target domain.This paper proposes three active learning scoring functions,namely consistency score,discriminator score and cosine difference score,which are designed to evaluate the labeling value of target domain samples from the perspectives of detection frame prediction inconsistency,domain belonging probability and feature difference,respectively.At the same time,a set of evaluation framework for object detection tasks is constructed,which considers the overall annotation effect of each image and quantifies the annotation cost of each detection frame.Experiments show that the proposed method can improve the object detection performance of active learning by about 3.29% on the target domain,and reduce the number of labeled bounding boxes required by the target domain by about 17.6%.Under the condition of limited annotation resources,this method can effectively improve the performance of object detection and domain migration,and can cope with the problem of model performance degradation caused by distribution differences between source and target domains,provide a new solution for cross-domain object detection in remote sensing scenes,and provide reliable data support for the construction of high-precision semantic datum maps,thereby improving the accuracy and reliability of scene matching navigation.

Key words: Scene matching, Object detection, Active learning, Domain adaptation, Discriminators