A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences
Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution i...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-02-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/11/4/470 |
id |
doaj-2600485127f6428fa23c56ec28330372 |
---|---|
record_format |
Article |
spelling |
doaj-2600485127f6428fa23c56ec283303722020-11-25T01:28:22ZengMDPI AGRemote Sensing2072-42922019-02-0111447010.3390/rs11040470rs11040470A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution DifferencesKai Li0Yongsheng Zhang1Zhenchao Zhang2Guangling Lai3PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, ChinaPLA Strategic Support Force Information Engineering University, Zhengzhou 450001, ChinaFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The NetherlandsPLA Strategic Support Force Information Engineering University, Zhengzhou 450001, ChinaAutomatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times.https://www.mdpi.com/2072-4292/11/4/470image registrationedge point featurenormalized mutual informationACO for continuous domaindiversity of parameters |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kai Li Yongsheng Zhang Zhenchao Zhang Guangling Lai |
spellingShingle |
Kai Li Yongsheng Zhang Zhenchao Zhang Guangling Lai A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences Remote Sensing image registration edge point feature normalized mutual information ACO for continuous domain diversity of parameters |
author_facet |
Kai Li Yongsheng Zhang Zhenchao Zhang Guangling Lai |
author_sort |
Kai Li |
title |
A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences |
title_short |
A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences |
title_full |
A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences |
title_fullStr |
A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences |
title_full_unstemmed |
A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences |
title_sort |
coarse-to-fine registration strategy for multi-sensor images with large resolution differences |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-02-01 |
description |
Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times. |
topic |
image registration edge point feature normalized mutual information ACO for continuous domain diversity of parameters |
url |
https://www.mdpi.com/2072-4292/11/4/470 |
work_keys_str_mv |
AT kaili acoarsetofineregistrationstrategyformultisensorimageswithlargeresolutiondifferences AT yongshengzhang acoarsetofineregistrationstrategyformultisensorimageswithlargeresolutiondifferences AT zhenchaozhang acoarsetofineregistrationstrategyformultisensorimageswithlargeresolutiondifferences AT guanglinglai acoarsetofineregistrationstrategyformultisensorimageswithlargeresolutiondifferences AT kaili coarsetofineregistrationstrategyformultisensorimageswithlargeresolutiondifferences AT yongshengzhang coarsetofineregistrationstrategyformultisensorimageswithlargeresolutiondifferences AT zhenchaozhang coarsetofineregistrationstrategyformultisensorimageswithlargeresolutiondifferences AT guanglinglai coarsetofineregistrationstrategyformultisensorimageswithlargeresolutiondifferences |
_version_ |
1725102223600386048 |