Abstract
Abstract. The aim of geometric matching is to extract the geometric transformation parameters between the corresponding images. That is useful for photogrammetric mapping, deformation detection, and flying platform's posture analyses, etc. It is different compare with ordinary feature based image matching succeed by selecting feature points correctly, the proposed method takes all the pixels within the corresponding images to participate the matching procedure for calculating the geometric parameters by least square criterion. The principle of the algorithm, such as the gray corresponding equation, the information quantity inequation and procedure of least square solution are introduced in detail. Particularly, the wavelet analyses for gray signal and calculating the information quantity by signal to noise ratio. Finally, a series of sequential images obtained by a low-altitude helicopter equipped with a video camera was used to test and verify the validity and reliability of the theory and algorithm in this paper. Two typical results are got according to the relative orientation elements model and parallax grid model. The conclusion is got in comparing APM with ordinary feature point method by the information quantity inequation.
Highlights
The geometric matching is happened in the case, that the available corresponding images are confirmed taking from a same object surface
The aim of image matching is not to estimate the degree of similarity, but for solving the geometric relationship to get the transformation parameters. It is useful for photogrammetric mapping, deformation detection, flying platform’s posture analyses, registration and mosaic of remote sensing images, etc
From information theory view, that the similarities and differences of the two methods can be described by information quantity inequation, the formula (7) or (12)
Summary
The geometric matching is happened in the case, that the available corresponding images are confirmed taking from a same object surface. The aim of image matching is not to estimate the degree of similarity, but for solving the geometric relationship to get the transformation parameters. It is useful for photogrammetric mapping, deformation detection, flying platform’s posture analyses, registration and mosaic of remote sensing images, etc. Some imperfections still exist, those methods are applied successful, effective and widely spread It is different compare with above mentioned feature point matching methods; the following proposed method takes all the pixels within the corresponding images to participate the matching procedure for calculation parameters of the geometric models by least square criterion, abbreviation as APM (All Pixels-participated Matching)
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