Research On Image Mosaic Method Of UAV Low-altitude Remote Sensing Images

UAV remote sensing is a new type of remote sensing technology and has the advantages of efficient and flexible maneuverability,fast operation speed,high cost and high resolution.After rapid development in recent years,the application field has been expanding.UAV remote sensing technology has a wide range of applications in many fields,including disaster reduction and disaster relief,agricultural plant protection,electricity inspection,land management,river surveys and so on.However,due to the drone remote sensing system in the process of aerial photography,it is often constrained by various factors such as flying height and camera focal length,resulting in a single image coverage area is relatively small,usually can not contain the entire required Area,therefore,in order to obtain the entire target area image information,it is necessary to splice and merge multiple acquired UAV remote sensing images into one panoramic image.Firstly,this paper briefly introduces the system composition of UAV,the research background and significance of UAV remote sensing image splicing technology,the research status of domestic and foreign image mosaic and UAV remote sensing image splicing,and then from the UAV remote sensing imagery.The splicing process elaborated the theoretical knowledge and experimental analysis of each splicing link.The main research content of this article is as follows:(1)Introduce the relevant theories of image mosaic technology,including the definition of image stitching,the basic flow,the classification of image registration technology,and the basic theoretical techniques and methods of image fusion.(2)Introduce three common feature point extraction algorithms—SIFT algorithm,SURF algorithm,and ORB algorithm—in detail,and analyze and compare the features of each feature point extraction algorithm through corresponding experiments.Finally,according to the characteristics of remote sensing images of UAVs,The ORB feature extraction algorithm was selected as the method to extract feature points of UAV remote sensing images.(3)The detailed theories and methods of the stitch image feature matching algorithm and the image fusion algorithm are introduced in detail,and a suitable processing method is selected for the processing of UAV remote sensing images.In the feature point matching,the rough matching of the KNN algorithm is firstly adopted.Then the improved RANSAC algorithm is used to eliminate the mismatched points and an improved fusion algorithm is used for image fusion.(4)According to the relevant theories and techniques of image splicing explained in the previous chapters,conduct a series of experiments on the splicing processing of UAV remote sensing images,and conduct experiments based on different sizes of UAV remote sensing images in feature point detection experiments.Quantitative analysis,comparing the detection effect and detection efficiency of three common feature point detection algorithms,finally selected the ORB algorithm as the feature detection algorithm;during feature matching experiments,KNN-RANSAC algorithm feature matching and improved KNN respectively.-RANSAC algorithm feature matching,and the two of the feature matching accuracy of the image using the medium error and mean square error quantitative analysis of the improved KNN-RANSAC algorithm matching accuracy has improved.In the process of image fusion experiments,the fusion experiments of gradual ingress and egress fusion experiments and improved fusion algorithms were performed respectively.Through the comparison of experimental results,it was found that the improved fusion algorithm can well solve the problem of splice gaps and local area blurs in the image fusion process..Finally,through the stitching method proposed in this paper,the seamless mosaic experiment of sequence remote sensing images is completed to obtain the panoramic image of the target area.