In order to accurately identify the aircrafts in remote sensing images,K-means algorithm is used to carry out a clustering analysis on the dataset based on YOLO V3 algorithm.By referring to Densenet theory,the two residual network modules in YOLO V3 are replaced by two dense network modules,and a Dense-YOLO Deep Convolution Neural Network (DCNN) is developed.The networks before and after improvement are trained respectively,and the weight files with which the two networks have the best recognition results are selected.The high-quality remote sensing images and the low-quality remote sensing images having the problems of over-exposure and cloud occlusions are tested and analyzed respectively.The results show that the application of the improved DCNN to the two kinds of images improves the recognition performance.In high-quality remote sensing images,the accuracy rate of the improved algorithm is as high as 99.72%,which is improved by 0.85%;the recall rate of the improved algorithm is as high as 98.34%,which is improved by 1.94%.In low-quality remote sensing images,the accuracy rate of the improved algorithm is as high as 96.12%,which is improved by 5.07%;the recall rate of the improved algorithm is as high as 93.10%,which is improved by 19.75%.