Abstract
This study presents an optimal weighting approach for combined image matching of high-resolution satellite stereo images (HRSI). When the rational polynomial coefficients (RPCs) for a pair of stereo images are available, some geometric constraints can be combined in image matching equations. Combining least squares image matching (LSM) equations with geometric constraints equations necessitates determining the appropriate weights for different types of observations. The common terms between the two sets of equations are the image coordinates of the corresponding points in the search image. Considering the fact that the RPCs of a stereo pair are produced in compliance with the coplanarity condition, geometric constraints are expected to play an important role in the image matching process. In this study, in order to control the impacts of the imposed constraint, optimal weights for observations were assigned through equalizing their average redundancy numbers. For a detailed assessment of the proposed approach, a pair of CARTOSAT-1 sub-images, along with their precise RPCs, were used. On top of obtaining different matching results, the dimension of the error ellipses of the intersection points in the object space were compared. It was shown through analysis that the geometric mean of the semi-minor and semi-major axis by our method was reduced 0.17 times relative to the unit weighting approach.
Highlights
Satellite image matching is an essential stage in the production of photogrammetric products, such as various large scale maps and digital surface models (DSMs)
Relying on the redundancy matrix, we controlled the amount of displacement of the points on both images during image matching iterations
A more reliable image matching strategy leads to a more accurate scene reconstruction, which is usually achieved by introducing geometric constraints
Summary
Satellite image matching is an essential stage in the production of photogrammetric products, such as various large scale maps and digital surface models (DSMs). There are different categorizations for image matching techniques. Methods of stereo image matching are divided into two groups, namely, global and local methods [2]. The local or window-based methods work on local windows in each point of the stereo images, while the global methods generate a depth map from entire images through defining and optimizing an energy function [1]. The global methods demonstrate better performance in comparison with local methods in dense matching, but their computational complexity is higher [3]. The semi-global methods have reduced computational complexity through the introduction of some simplifications in optimization algorithms of the global methods, such as semi-global matching (SGM) [4], tSGM [3], and SGM-Nets [5]
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