Next Article in Journal
Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands
Previous Article in Journal
A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(9), 965;

Optimal Weight Design Approach for the Geometrically-Constrained Matching of Satellite Stereo Images

School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, 1439957131 Tehran, Iran
Author to whom correspondence should be addressed.
Received: 13 June 2017 / Revised: 12 August 2017 / Accepted: 13 September 2017 / Published: 18 September 2017
(This article belongs to the Section Remote Sensing Image Processing)
Full-Text   |   PDF [4028 KB, uploaded 19 September 2017]   |  


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. View Full-Text
Keywords: constrained image matching; HRSI; weight design; RPC model constrained image matching; HRSI; weight design; RPC model

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Afsharnia, H.; Arefi, H.; Sharifi, M.A. Optimal Weight Design Approach for the Geometrically-Constrained Matching of Satellite Stereo Images. Remote Sens. 2017, 9, 965.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top