Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11030
For citation please use:
Main Title: Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation
Author(s): Ye, Zhen
Kang, Jian
Yao, Jing
Song, Wenping
Liu, Sicong
Luo, Xin
Xu, Yusheng
Tong, Xiaohua
Type: Article
Language Code: en
Abstract: Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L1-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.
URI: https://depositonce.tu-berlin.de/handle/11303/12156
http://dx.doi.org/10.14279/depositonce-11030
Issue Date: 4-Aug-2020
Date Available: 9-Dec-2020
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Subject(s): image registration
subpixel matching
phase correlation
multisensor remote sensing images
fine registration
Sponsor/Funder: TU Berlin, Open-Access-Mittel – 2020
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Sensors
Publisher: MDPI
Publisher Place: Basel
Volume: 20
Issue: 15
Article Number: 4338
Publisher DOI: 10.3390/s20154338
EISSN: 1424-8220
Appears in Collections:FG Remote Sensing Image Analysis Group » Publications

Files in This Item:
sensors-20-04338-v2.pdf
Format: Adobe PDF | Size: 4.85 MB
DownloadShow Preview
Thumbnail

Item Export Bar

This item is licensed under a Creative Commons License Creative Commons