Automatic UAV image geo-registration by matching UAV images to georeferenced image data

Xiangyu Zhuo, Tobias Koch, Franz Kurz, Friedrich Fraundorfer, Peter Reinartz

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles). As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s) with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model) attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.

Original languageEnglish
Article number376
JournalRemote Sensing
Volume9
Issue number4
DOIs
Publication statusPublished - 1 Apr 2017

Fingerprint

registration
vehicle
orthophoto
GNSS
nadir
image resolution
photogrammetry
method
experiment
co-ordinate system
detection
satellite image

Keywords

  • Geo-registration
  • Image registration
  • Point cloud
  • Unmanned aerial vehicle

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Cite this

Automatic UAV image geo-registration by matching UAV images to georeferenced image data. / Zhuo, Xiangyu; Koch, Tobias; Kurz, Franz; Fraundorfer, Friedrich; Reinartz, Peter.

In: Remote Sensing, Vol. 9, No. 4, 376, 01.04.2017.

Research output: Contribution to journalArticleResearchpeer-review

Zhuo, Xiangyu ; Koch, Tobias ; Kurz, Franz ; Fraundorfer, Friedrich ; Reinartz, Peter. / Automatic UAV image geo-registration by matching UAV images to georeferenced image data. In: Remote Sensing. 2017 ; Vol. 9, No. 4.
@article{fb5af44ea1f445e7ad1fcf79587300b2,
title = "Automatic UAV image geo-registration by matching UAV images to georeferenced image data",
abstract = "Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles). As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s) with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model) attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.",
keywords = "Geo-registration, Image registration, Point cloud, Unmanned aerial vehicle",
author = "Xiangyu Zhuo and Tobias Koch and Franz Kurz and Friedrich Fraundorfer and Peter Reinartz",
year = "2017",
month = "4",
day = "1",
doi = "10.3390/rs9040376",
language = "English",
volume = "9",
journal = "Remote sensing",
issn = "2072-4292",
publisher = "MDPI AG",
number = "4",

}

TY - JOUR

T1 - Automatic UAV image geo-registration by matching UAV images to georeferenced image data

AU - Zhuo, Xiangyu

AU - Koch, Tobias

AU - Kurz, Franz

AU - Fraundorfer, Friedrich

AU - Reinartz, Peter

PY - 2017/4/1

Y1 - 2017/4/1

N2 - Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles). As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s) with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model) attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.

AB - Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles). As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s) with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model) attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.

KW - Geo-registration

KW - Image registration

KW - Point cloud

KW - Unmanned aerial vehicle

UR - http://www.scopus.com/inward/record.url?scp=85017621129&partnerID=8YFLogxK

U2 - 10.3390/rs9040376

DO - 10.3390/rs9040376

M3 - Article

VL - 9

JO - Remote sensing

JF - Remote sensing

SN - 2072-4292

IS - 4

M1 - 376

ER -