Research On Image Matching Algorithm Based On Improved ORB And Improved SURF

With the emergence of sallite remote sensing data, the fficiency and quality of UAV image acquisition have been improved steaily. UAV remote sensing tchnology and saellite| SAR have made great progress in the application field. UAV remote sensing technology and satellite SAR both have certain technical limitations, and usually need to be used collaboratively in disaster identification, monitoring, assessment, and terrain feature extraction and monitoring under special terrain, which poses a new challenge to the accuracy and timeliness of image processing technology. Image matching is an important link in image processing technology, and it is also a hotspot researched by researchers at home and abroad. In order to achieve fast and accuratec image matching for UAV images and SAR images, two fast ORB algorithm and SURF algoritm are selected to improve the speed. Aiming at the problems of few exact matching points and low matching accuracy in the ORB algorithm, it was improved by replacing the descriptor of the original algorithm with BEBLID descriptor. And aiming at the problem of accurate matching points in SURF algorithm, it was improved by replacing the descriptors of original algorithm with’ DEEPDESC deseriptors. In order to verify the matching performance and robustness performance of the two improved algorithms, this paper conducts the UAV image matching experiment, sentinel one image matching experiment in stellitle SAR and robustness verification based on grafiti data set, by using the improved algorithm, the original algorithm, the conventional algorithm and the deep learning-based LIFT algorithm. The six algorithms are analyzed and compared from five aspects: matching result, matching time, precise matching points, matching accuracy and matching precision. The main conclusions are as follows:(1) Explore the matching performance and applicability of improved ORB algorithm and improved SURF algorithm in UAV image matching. The experimental results show that when perlorming UAV image matching, there are 197 more pairs of precision mathing points in the improved ORB algrithm than that in the ORB algorihm, which increascs by(2)Explore the matching performance and applicability of improved ORB algorithm and improved SURF algorithm in Sentinel-1 image matching.The experimental results show that when performing image of Sentinel-1 matching,there are 184 more pairs of mean exact matching points in the improved ORB algorithm than in the ORB algorithm,which increases by 20.67%and the average matching rate increases by 9.23%;the average matching rate of the improved SURF algorithm is 3.28%higher than that of the SURF algorithm,the average number of matching point pairs is 244 more than that of SURF algorithm,which increases by19.67%.Compared with the six algorithms,the improved ORB algorithm is the best one among the six algorithms for the high level time-efficient Sentinel-1 image matching,and the improved SURF algorithm has the largest number of matching points and the highest matching accuracy.The improved SURF algorithm is the best choice when there is high demand for these two aspects,and the improved SURF algorithm and LIFT algorithm both have good performance when there is large area of water body in Sentinel-1 image.(3)The robustness of the six algorithms is verified under different luminance,blur,rotation and affine distortion.The experiments show that the ORB algorithm can adapt to the brightness distortion well,but not to the blur,rotation and affine distortion;The adaptability and matching accuracy of the improved ORB algorithm for brightness,ambiguity,rotation and affine deformation are improved;SURF algorithm is not suitable for fuzzy and affine deformation;The improved SURF algorithm has the best adaptability to affine deformation,and its matching precision is better than SURF algorithm;AKAZE algorithm has the best adaptability to fuzzy distortion,and the adaptability to rotation,brightness and affine distortion is moderate among the six algorithms,but the matching accuracy can keep stable;The LIFT algorithm has the best adaptability to the luminance distortion.