ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 121–125, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-121-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 121–125, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-121-2020

  03 Aug 2020

03 Aug 2020

A LEAST SQUARE ALGORITHM FOR GEOMETRIC MATCHING OF REMOTE SENSED IMAGES

Y. Yang1, G. Su2, Y. Li2, F. Liu3, and Z. Lin2 Y. Yang et al.
  • 1National Geomatics Center of China, Beijing, China
  • 2Chinese Academy of Surveying and Mapping, Beijing, China
  • 3Beijing Institute of Surveying and Mapping, Beijing, China

Keywords: Image matching, Geometric matching, Wavelet analyses, All pixels Participate Matching, Gray corresponding equation, Information quantity inequation

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.