A High-Efficiency Aircraft Detection Approach Utilizing Auxiliary Information in Sar Images

Aircraft detection in synthetic aperture radar (SAR) image is a special case because all the targets are located in the airport. Comparing with the whole scene SAR image, the area of an airport is relatively small, therefore, this information can be utilized to speed up the algorithm. This paper proposes a centroid network detection method based on the combination of geographic coordinate information and subscene classification. Firstly, the airport area is detected based on the priori geographic coordinate information. Secondly, to further narrow down the scope of detection and extract the regions containing valid targets, the subscene is fed into the ResNet50 network which incorporates Squeeze and Excitation (SE) to separate the aircraft area from the background area. The method is validated in ablation experiments on the GaoFen-3(GF3) datasets to reveal the impact of each factor. The results show that the proposed method can achieve a reduction in false alarm rate around 6% and time cost around 30%.