基于多尺度圆周频率滤波与卷积神经网络的遥感图像飞机目标检测方法研究

针对遥感图像飞机目标检测因目标尺度不一存在漏警、虚警等问题，该文基于遥感图像中飞机目标形状特征和灰度变化特点提出了一种多尺度圆周频率滤波(MSCFF)与卷积神经网络(CNN)相结合的MSCFF+CNN飞机目标自动检测算法。该算法首先采用多尺度圆周频率滤波器滤除遥感图像复杂背景，实现不同尺度飞机目标候选区域提取；然后，通过构建卷积神经网络(CNN)模型实现候选区域有效分类，最终精确确定飞机目标位置。最后，基于获取的真实遥感图像进行目标检测算法实验验证，经统计该算法的飞机目标检测率为94.38%，虚警率为3.76%，实验结果充分验证了该文算法的有效性，该算法可为机场监管、军事侦察等应用提供重要的技术支持。

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.