A sub-pixel geometric evaluation for very high-resolution satellite images using phase cross-correlation algorithm

The mosaic image is an essential process for merging several images, especially for very high-resolution satellite images, as its width swath is small. The main issue of mosaic images is the geometric accuracy between the images. This paper proposed a sub-pixel geometric evaluation to assess the geometric shift between the images. It is an essential step for image registration. Image registration is a process to determine a geometric shift involving two or more images with the same object but different acquisition dates, viewpoints, and sensors. The basic idea of image registration in the frequency domain is estimating the shift between the test image and the reference image. In this study, we used the phase cross-correlation function to determine the geometric shift between two different acquisition Pleiades images from each polygon. Phase correlation is based on the translation property of the Fourier Transform. It transforms the displacement of two correlated images in the spatial domain into a phase difference in the frequency domain. The phase correlation algorithm calculates a phase difference map containing a single peak. The peak location is proportional to the value of the shift between the two images. Several Pleiades images were used in the experiment. The image processing level is an ortho-image, which is already pan-sharped. As a result, the highest geometric shift is on Polygon 2 based on the R-value, and the lowest error value is in polygon 1. The experiments also showed that the most significant error value is in the blue band, and the lowest is in the red band. The average geometric shift between the two Pleiades data is 1.187 pixels, with the most shift located in the blue band with a 1.282 pixels shift, and the Root Mean Square Error (RMSE) is 1.8.