Airport Detection Method Combined with Continuous Learning of Residual-Based Network on Remote Sensing Image

In the existing high-resolution and large-scale target remote sensing image object detection, the traditional method cannot achieve airport target recognition from optical remote sensing images quickly and accurately due to the single feature extraction and slow speed. Inspired by the hierarchical cognition of the human visual system, the continuous learning of residual-based convolution neural network (CLRNet) suitable for medium and high resolution optical remote sensing images is proposed. Firstly, the depth residual block is constructed as the feature extraction network. Secondly, the continuous learning method is used to fine tune the airport detection model from the massive remote sensing data. After continuous learning process, the model with strong robustness and low forgetting degree is obtained. The model can accurately and quickly identify airport from optical remote sensing images under massive and complex backgrounds. Our model has a better recognition effect for airports covered by thin clouds or incompletely captured by satellites. The domestic Jilin-1 satellite image dataset is selected for testing. Experiments show that the accuracy of the detection method mAP (IoU is not less than 0. 5) can reach 0.9613, and the detection speed can reach 0.23 s per scene.