Object Recognition for Remote Sensing Images from UAVs via Convolutional Neural Networks

In recent years, researches of remote sensing data have begun to develop towards mass information and diversification, and traditional analytical methods alone could not meet the needs of modern data processing. Deep learning methods have developed a lot in the field of computer vision which has provided a new target recognition method for remote sensing images. This paper concentrates on the application of convolutional neural networks(CNN) in object recognition of Unmanned Aerial Vehicle(UAV) remote sensing images: YOLOv3 and Faster R-CNN network model are built on computer and NVIDIA JETSON TX2, and NWPU VHR-10 dataset is used as training and test samples for simulation verification. The results show that the accuracy of remote sensing multiclass target recognition using the YOLOv3 network model is 73.8%, and the detection rate is 0.25s per image on average. The accuracy of using YOLOv3 network model in several network models is second only to Faster RCNN, but its real-time performance is better than one of Faster RCNN. The YOLOv3 network model is more appropriate for carrying on the UAVs.