Abstract
Due to the limited accuracy of exterior orientation parameters, ground control points (GCPs) are commonly required to correct the geometric biases of remotely-sensed (RS) images. This paper focuses on an automatic matching technique for the specific task of georeferencing RS images and presents a technical frame to match large RS images efficiently using the prior geometric information of the images. In addition, a novel matching approach using online aerial images, e.g., Google satellite images, Bing aerial maps, etc., is introduced based on the technical frame. Experimental results show that the proposed method can collect a sufficient number of well-distributed and reliable GCPs in tens of seconds for different kinds of large-sized RS images, whose spatial resolutions vary from 30 m to 2 m. It provides a convenient and efficient way to automatically georeference RS images, as there is no need to manually prepare reference images according to the location and spatial resolution of sensed images.
Highlights
Direct geo-location of remotely-sensed (RS) images is based on the initial imaging model, e.g., rigorous sensor model and Rational Polynomial Coefficients (RPC) model without ground control, and the accuracy of the model is limited by the interior and exterior orientation parameters.The accurate interior orientation parameters can be achieved by performing on-board geometric calibration, but the exterior orientation parameters, which are directly observed by on-board GPS, inertial measuring units and star-trackers, usually contain variable errors
It is a very convenient and efficient way to automatically collect ground control points (GCPs) for the task of geometric rectification of RS images, as there is no need to manually prepare reference images according to the location and spatial resolution of sensed images
We proposed a convenient approach to automatically collect GCPs from online aerial maps, which focuses on automated georeferencing of remotely-sensed (RS) images and makes use of the prior information of the RS image
Summary
Direct geo-location of remotely-sensed (RS) images is based on the initial imaging model, e.g., rigorous sensor model and Rational Polynomial Coefficients (RPC) model without ground control, and the accuracy of the model is limited by the interior and exterior orientation parameters. Compared to intensity correlation methods, phase correlation methods have many advantages, including high discriminating power, numerical efficiency, robustness against noise [10] and high matching accuracy [11] It is difficult for phase correlation methods to be extended to match images with more complicated deformation, Fourier–Mellin transformation can be applied to deal with translated, rotated and scaled images [12]. SIFT-based methods face the following challenges when directly used in RS images: large image size, large scene, multi-source images, accuracy, distribution of matched points, outliers, etc. (i) a convenient approach to perform point matching for RS images using online aerial images; (ii) a technical frame to find uniformly-distributed control points for large RS images efficiently using the prior geometric information of the images; and (iii) an improved strategy to match SIFT features and eliminate false matches.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
