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Proceedings Paper

An iterative SIFT based on intensity and spatial information for remote sensing image registration
Author(s): Shuhan Chen; Xiaorun Li; Liaoying Zhao; Chein-I Chang; Bai Xue
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Paper Abstract

Owing to significant geometric distortions and illumination differences, high precision and robust matching of multisource remote sensing image registration poses a challenge. This paper presents a new approach, called iterative scale invariant feature transform (ISIFT) with rectification (ISIFTR), to remote sensing image registration. Unlike traditional SIFT-based methods or modified SIFT-based methods, the ISIFTR includes rectification loops to obtain rectified parameters in an iterative manner. The SIFT-based registration results is updated by rectification loops iteratively and terminated by an automatic stopping rule. ISIFTR works in three stages. The first stage is used to capture consistency feature sets with maximum similarity followed by a second stage to compare the registration parameters between two successive iterations for updating and finally concluded by a third stage to terminate the algorithm. The experimental results demonstrate that ISIFTR performed better registration accuracy than SIFT without rectification. By comparing the iteration curve based on the four different similarity metric, the results illustrate that the RIRMI-based rectification obtains better results than other similarity metrics.

Paper Details

Date Published: 14 May 2019
PDF: 7 pages
Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861O (14 May 2019); doi: 10.1117/12.2518710
Show Author Affiliations
Shuhan Chen, Zhejiang Univ. (China)
Xiaorun Li, Zhejiang Univ. (China)
Liaoying Zhao, Hangzhou Dianzi Univ. (China)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)
Bai Xue, Univ. of Maryland, Baltimore County (United States)

Published in SPIE Proceedings Vol. 10986:
Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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