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A Hierarchical Image Matching Method for Stereo Satellite Imagery : Volume Xl-7/W1, Issue 1 (12/07/2013)

By Yan, F.

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Book Id: WPLBN0004015732
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: A Hierarchical Image Matching Method for Stereo Satellite Imagery : Volume Xl-7/W1, Issue 1 (12/07/2013)  
Author: Yan, F.
Volume: Vol. XL-7/W1, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus Publications
Publication Date:
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications


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Wang, W., Chen, W., Yan, F., & Liu, S. (2013). A Hierarchical Image Matching Method for Stereo Satellite Imagery : Volume Xl-7/W1, Issue 1 (12/07/2013). Retrieved from

Description: College of Surveying and Geo-Informatics, Tongji University, Siping Road, Shanghai, China. Image matching is an essential and difficult task in digital photogrammetry and computer vision. This paper presents a triangulationbased hierarchical image matching algorithm for stereo satellite imagery. It uses a coarse-to-fine hierarchical strategy and combines feature points and grid points to provide a dense, precise and reliable matching result. First, some seed points are extracted at the top level of image pyramid using the SIFT algorithm with RANSAC approach to remove mismatches and enhance robustness. These points are used to construct an initial triangulation. Then, feature point and grid point matching are conducted based on the triangle constraint. In the process of hierarchical image matching, the parallaxes from upper levels are transferred to levels beneath with triangle constraint and epipolar geometrical constraint. At last, outliers are detected and removed based on local smooth constraint of parallax. Also, bidirectional image matching method is adopted to verify the matching results and increase the number of matched points. Experiments with ALOS images show that the proposed method has the capacity to generate reliable and dense matching results for surface reconstruction from stereo satellite imagery.



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