Research On Matching Strategy Based On Image Characteristics Of Low Altitude UAV

With the rapid development of computer vision technology and low-altitude Unmanned aerial vehicle(UAV),it has brought about tremendous changes to traditional aerial photogrammetry.UAV combined with remote sensing technology,drones rely on mobility and low-altitude flight.A series of advantages,such as high reliability,high security and low cost,make it possible to quickly acquire image data of the survey area in a short time,which is not available in the traditional aerial photogrammetry platform.Low-altitude UAV photogrammetry has been widely used in various fields of related industries,but low-altitude UAV images have their own'characteristics.Their low flying and small image range make the single image cover less,Vulnerable to external factors and other issues.These problems lead to the traditional image matching method,which has certain limitations in the matching application of low-altitude drone image data.Therefore,this paper mainly improves the feature matching method,which makes the time matching and robustness in image matching,and studies the matching strategy applicable to different regions of image data.1.According to the existing image local feature matching methods,the representative traditional feature matching methods are selected.,and the experimental data verification of low-altitude UAV image data is carried out.The results show that the existing traditional matching method is difficult to better balance the robustness and real-time performance of.low-altitude UAV image matching.2.In the face of the limitation of existing feature descriptors,based on SURF feature detection,a method of feature learning descriptors combined with triple sample shallow convolutional neural network(TFEAT)is proposed for UAV image matching.In the experimental data of low-altitude UAV in three different regions,the suitable data sample training method is used to obtain a well-adapted model.After verification,the improvement method is improved by at least 6%compared with the traditional method in the matching rate of low-altitude UAV.3.For the low-altitude UAV image data of three regional types,the image feature points are unevenly distributed,the feature points are not under the weak texture condition,and the number of mismatches in the weak texture environment is large,and the block-based multi-level is introduced.Combine image matching strategies to provide certain feature matching constraints and optimizations for the image matching process.Through experiments,it's found that the strategy of this paper improves the uniform distribution of features to a certain extent,and reduces the image matching residual.