A Hybrid Deep Neural Network Approach for Robust Multi-Scale Object Detection in SAR Images

Abstract

Synthetic Aperture Radar (SAR) images always provide high-resolution data in all weather and lighting circum-stances. However, speckle noise, clutter backgrounds and scale variation remain as significant challenges for accurate target detection on SAR images. This paper proposes a hybrid deep learning method, which combines Convolutional Neural Networks (CNN), STDNet model and CFAR-based detection, with the objective to enhance performance of multi-scale object detection. The outputs from both the CNN and STDNet branches are fused using an Intersection over Union (IoU)-based fusion strategy helps to achieve better detection accuracy. The experimental results show that the proposed hybrid model reaches better precision, recall, and F1-score than separate classifiers. This proposed approach is a cost-effective and practical strategy for tracking different real-world SAR targets.