A new optimization framework for the geometric positioning accuracy of optical images

Obtaining accurate geometric positioning information for images is crucial for the application of remote sensing images in stereo mapping, map production, and target positioning. However, for optical remote sensing satellite images and archived historical images, the geometric positioning methods vary, and different control data formats are needed. This limits the timeliness of applying optical remote sensing images and has become a bottleneck for the unified and standardized processing of such images. In response, this paper presents a framework for optimizing the geometric positioning accuracy of optical remote sensing images. A lightweight vector control library (LVCL) is constructed to serve as the control data for the geometric positions of the optical images, and a rough positioning method based on a mutual feedback constraint-matching strategy and multiple associated images is proposed to obtain approximate positions of the target images quickly. Moreover, optimal iteration and segmented connection based vector matching methods are proposed to accurately match the target image to images in the LVCL, thereby providing the target image’s geographical information based on the control data. Experiments conducted across multiple regions show that this framework can effectively match remote sensing images with the geographic coordinates in the LVCL: the data storage volume is only 1/10 to 1/3893 of that of traditional and existing lightweight methods, demonstrating prominent lightweight advantages; among the images from 13 cities, the correct rough positioning matching results for 12 cities rank among the top 10, with 7 achieving the first place; the number of vector construction nodes in 62 regions is better than that of the comparison strategy; the vector matching results are correct and the number of matching points is the largest; for the 13 regions, the average positioning deviation decreases from 156.590 m to 4.098 m, and the maximum deviation decreases from 582.142 m to 10.588 m, with systematic errors significantly corrected. Thus, the proposed framework is expected to become a new paradigm for obtaining the geometric positions of optical remote sensing images.