Multi Scale SAR Aircraft Detection Based on Swin Transformer and Adaptive Feature Fusion Network

Abstract

Aircraft detection in synthetic aperture radar (SAR) image is a very important but challenging question. Due to the multi-scale characteristics of aircraft and the complex background of airports in SAR images, the detection process often encounters challenges of false alarms and missed detections. Meanwhile, the presence of SAR image noise and the discrete distribution characteristics of scatterers can also contribute to incomplete aircraft target detection results. To address these problems, we proposed a novel multi-scale approach based on Swin Transformer. It employs a shifted window-based self-attention mechanism to extract the correlated features between scatter points. Moreover, to better enhance and integrate the multi-scale information among various level features, we incorporated an enhanced neck network with a four-layer feature pyramid and proposed an Adaptive Feature Fusion Network (AFFN) in our approach. Experiments are conducted on the GaoFen-3 (GF3) SAR aircraft datasets, and the results show the effectiveness of the proposed method.