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A Robust False Matching Points Detection Method for Remote Sensing Image Registration : Volume Xl-7/W3, Issue 1 (29/04/2015)

By Shan, X. J.

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Book Id: WPLBN0004015999
Format Type: PDF Article :
File Size: Pages 4
Reproduction Date: 2015

Title: A Robust False Matching Points Detection Method for Remote Sensing Image Registration : Volume Xl-7/W3, Issue 1 (29/04/2015)  
Author: Shan, X. J.
Volume: Vol. XL-7/W3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Historic
Publication Date:
2015
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Shan, X. J., & Tang, P. (2015). A Robust False Matching Points Detection Method for Remote Sensing Image Registration : Volume Xl-7/W3, Issue 1 (29/04/2015). Retrieved from http://www.hawaiilibrary.net/


Description
Description: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Chao Yang, Beijing 100101, China. Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false matching points in some remote sensing images. Therefore, a robust false matching points detection method based on Knearest- neighbour (K-NN) graph (KGD) is proposed in this method to obtain robust and high accuracy result. The KGD method starts with the construction of the K-NN graph in one image. K-NN graph can be first generated for each matching points and its K nearest matching points. Local transformation model for each matching point is then obtained by using its K nearest matching points. The error of each matching point is computed by using its transformation model. Last, L matching points with largest error are identified false matching points and removed. This process is iterative until all errors are smaller than the given threshold. In addition, KGD method can be used in combination with other methods, such as RANSAC. Several remote sensing images with different resolutions and terrains are used in the experiment. We evaluate the performance of KGD method, RANSAC + KGD method, RANSAC, and Graph Transformation Matching (GTM). The experimental results demonstrate the superior performance of the KGD and RANSAC + KGD methods.

Summary
A Robust False Matching Points Detection Method for Remote Sensing Image Registration

 

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