Aircraft Target Detection of Remote Sensing Images Based on Deep Neural Network
摘要
The airplane target detection of remote sensing images is frequently faced with problems including complex background and great changes of target scales.To address the problems,this paper proposes a model DC-DNN based on deep neural networks for aircraft detection in remote sensing images.The bottom layer features of images are used to make pixel-level labels for the training of Fully Convolutional Neural Network(FCN).The FCN model and DBSCAN algorithm are combined to select the self-adaptive candidate regions of the aircraft target,and the high-level features of the candidate region are extracted based on VGG-16 net to obtain the detection frame of the aircraft target.Also,a new detection frame suppression algorithm is proposed to eliminate overlapping frames and false detection frames to obtain the final detection result of the aircraft target.Experimental results show that the proposed DC-DNN model has the accuracy of aircraft target detection in remote sensing images reaching 95.78%,recall reaching 98.98%,and F1 score reaching 0.973 5,and it has better detection performance and generalization capabilities than WS-DNN,R-FCN and other models.