Aircraft Target Detection in Remote Sensing Image Based on Multi-scale Circle Frequency Filter and Convolutional Neural Network

In view of the problems of missed alarm and false alarm caused by the different scales of aircrafts in aircraft target detection tasks for remote sensing images, a Multi-Scale Cirale Frequency Filter (MSCFF) and Convolutional Neural Network (CNN) aircraft target automatic detection algorithm is proposed based on the shape characteristics and gray-scale changes of aircraft targets. Firstly, the multi-scale circle frequency filter is used to filter out the complex background of remote sensing images to extract the candidate region of aircraft targets on different scales. Then, the Convolutional Neural Network (CNN) model is constructed to realize the effective classification of candidate regions, and finally the aircraft target position is accurately determined. The target detection algorithm is experimentally verified based on the obtained real remote sensing images. It shows that the aircraft target detection rate and the false alarm rate are 94.38% and 3.76% respectively. The experimental results fully verify the effectiveness of the proposed algorithm, which can provide important technical support for airport supervision, military reconnaissance and other applications.