在现有的高分辨率、大尺度目标遥感图像的检测中,传统方法由于提取特征手段单一、速度慢而无法快速并准确地从光学遥感影像中实现机场目标的识别。受人类视觉系统层次认知的启发,提出了一种适用于中高分辨率光学遥感影像的机场目标检测网络(CLRNet)。首先构建深度残差块,并将其作为特征提取网络;然后基于生成的样本核心集,采用连续学习方式从海量遥感数据中逐次迭代,精调机场检测模型;经过连续学习得到了鲁棒性强、遗忘度低的检测模型,该模型可以准确快速地从海量复杂背景下的光学遥感影像中识别出机场目标,而且对薄云遮挡以及卫星拍摄不全的机场有较好的识别效果。选取国产吉林一号卫星影像数据集进行测试,结果表明:所提方法的检测精度mAP(IoU不小于0.5)可达0.9613,每景的检测时间为0.23 s。

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.