A Novel Data Augmentation Method for SAR Image Target Detection and Recognition

Abstract

With the development of remote sensing satellite technology, the resolution of remote sensing images is constantly improved, but there are difficulties in obtaining labeled SAR image datasets for target detection and recognition. To address the problem that only limited SAR image target detection and recognition data are available, a novel data augmentation method based on convolutional neural network is proposed. Firstly, the Synthetic Aperture Radar (SAR) image target detection and recognition dataset SAR_OD was produced based on the synthesis of military targets and background images in MSTAR dataset. But considering the fact that the number of targets in each image in SAR_OD is still not enough for training a target detection model with good performance, we augmented SAR_OD and then we obtained SAR_OD+ dataset. It is proved that the model trained on SAR_OD+ dataset is significantly improved in the evaluation index by the data augmentation method proposed in this paper, especially in the experiments using only 50% of the training data. Therefore, the proposed data augmentation method can be used to improve the performance of SAR image target detection and recognition model in the case of limited labeled data.