针对亚米级高分辨率遥感影像中机场识别算法存在的定位精度和识别准确率低的问题，提出了一种基于深度卷积神经网络的高分辨率遥感影像机场识别算法。首先，使用双三次插值算法对原始影像进行下采样处理并转为灰度图，进行模糊增强以得到预处理图像。其次，利用Canny算子提取灰度图边缘信息并使用概率Hough变换提取其中的直线，通过判断平行线存在与否对直线区域进行初步筛选及合并。再次，对合并后的区域利用深度卷积神经网络进行判别以得到相应区域的识别概率值。最后，通过分析概率值得到机场目标。对某卫星两种高分辨率遥感影像数据进行实验，得到识别率100%、定位准确率87.53%的实验结果，证明了所提算法的有效性和通用性。

In order to solve the problems of low locating precision and low recognition rate of the airport identification algorithm in sub-meter high-resolution remote sensing images, a new identification algorithm based on Deep Convolutional Neural Network (DCNN) is proposed.Firstly, the bi-cubic interpolation algorithm is used to down-sample the original single-phase remote sensing images and convert them into grayscale images, and the pre-processed images are obtained by fuzzy enhancement.Secondly, the edge information of the images is detected by using Canny edge detection operator, and the straight line segments are extracted by using probability Hough transform.The linear regions are preliminarily screened and merged by judging whether there are parallel lines. Then, DCNN is used for judging the merged regions to acquire the recognition probability of the corresponding regions. Finally, the airport area is obtained by analyzing the probability values of the candidate regions.Simulation experiments were made to the two kinds of remote sensing images with high resolution, the recognition rate was 100% and the mean locating accuracy was 87.53%, which proved the validity and versatility of the proposed algorithm.