Controlled PCA-Based Generation of Synthetic Object Images for Satellite SAR Data Augmentation

Nowadays, synthetic aperture radars are widely used on various satellite imaging platforms. The high availability of SAR imagery makes it very popular in various automatic object detection systems. However, the visual interpretation of SAR data is difficult even for experts, which poses the primary challenge in preparing the data required for training and evaluating deep learning models. Therefore, using data augmentation techniques to expand the original training dataset is a popular approach. In this paper, a method of synthetic SAR object image generation is proposed using Principal Component Analysis. The presented technique is based on principal component perturbations and can be used for data augmentation to train object detection models. The numerical experiments performed with the YOLO model showed that the proposed method improved AP50 on the test set for transport aircraft detection, with statistical significance confirmed by bootstrap analysis.