Improving FOSS photogrammetric workflows for processing large image datasets

Oscar Martinez-rubi, Francesco Nex, Marc Pierrot-deseilligny, Ewelina Rupnik

Research output: Contribution to journalArticleAcademicpeer-review

32 Downloads (Pure)

Abstract

Background: In the last decade Photogrammetry has shown to be a valid alternative to LiDAR techniques for the generation of dense point clouds in many applications. However, dealing with large image sets is computationally demanding. It requires high performance hardware and often long processing times that makes the photogrammetric point cloud generation not suitable for mapping purposes at regional and national scale. These limitations are partially overcome by commercial solutions, thanks to the use of expensive and dedicated hardware. Nonetheless, a Free and Open-Source Software (FOSS) photogrammetric solution able to cope with these limitations is still missing.

Methods: In this paper, the bottlenecks of the basic components of photogrammetric workflows -tie-points extraction, bundle block adjustment (BBA) and dense image matching- are tackled implementing FOSS solutions. We present distributed computing algorithms for the tie-points extraction and for the dense image matching. Moreover, we present two algorithms for decreasing the memory needs of the BBA. The various algorithms are deployed on different hardware systems including a computer cluster.

Results and conclusions: The usage of the algorithms presented allows to process large image sets reducing the computational time. This is demonstrated using two different datasets.
Original languageEnglish
Article number12
Number of pages13
JournalOpen Geospatial Data, Software and Standards
Volume2
DOIs
Publication statusPublished - 1 Dec 2017

Fingerprint

Image matching
Processing
Computer hardware
Photogrammetry
Distributed computer systems
Computer systems
Hardware
Data storage equipment
Open source software

Keywords

  • ITC-GOLD

Cite this

Martinez-rubi, Oscar ; Nex, Francesco ; Pierrot-deseilligny, Marc ; Rupnik, Ewelina. / Improving FOSS photogrammetric workflows for processing large image datasets. In: Open Geospatial Data, Software and Standards. 2017 ; Vol. 2.
@article{ea822f9bb2a34cf181bb9cc2c415a29e,
title = "Improving FOSS photogrammetric workflows for processing large image datasets",
abstract = "Background: In the last decade Photogrammetry has shown to be a valid alternative to LiDAR techniques for the generation of dense point clouds in many applications. However, dealing with large image sets is computationally demanding. It requires high performance hardware and often long processing times that makes the photogrammetric point cloud generation not suitable for mapping purposes at regional and national scale. These limitations are partially overcome by commercial solutions, thanks to the use of expensive and dedicated hardware. Nonetheless, a Free and Open-Source Software (FOSS) photogrammetric solution able to cope with these limitations is still missing.Methods: In this paper, the bottlenecks of the basic components of photogrammetric workflows -tie-points extraction, bundle block adjustment (BBA) and dense image matching- are tackled implementing FOSS solutions. We present distributed computing algorithms for the tie-points extraction and for the dense image matching. Moreover, we present two algorithms for decreasing the memory needs of the BBA. The various algorithms are deployed on different hardware systems including a computer cluster.Results and conclusions: The usage of the algorithms presented allows to process large image sets reducing the computational time. This is demonstrated using two different datasets.",
keywords = "ITC-GOLD",
author = "Oscar Martinez-rubi and Francesco Nex and Marc Pierrot-deseilligny and Ewelina Rupnik",
year = "2017",
month = "12",
day = "1",
doi = "10.1186/s40965-017-0024-5",
language = "English",
volume = "2",
journal = "Open Geospatial Data, Software and Standards",
issn = "2363-7501",
publisher = "SpringerOpen",

}

Improving FOSS photogrammetric workflows for processing large image datasets. / Martinez-rubi, Oscar; Nex, Francesco; Pierrot-deseilligny, Marc; Rupnik, Ewelina.

In: Open Geospatial Data, Software and Standards, Vol. 2, 12, 01.12.2017.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Improving FOSS photogrammetric workflows for processing large image datasets

AU - Martinez-rubi, Oscar

AU - Nex, Francesco

AU - Pierrot-deseilligny, Marc

AU - Rupnik, Ewelina

PY - 2017/12/1

Y1 - 2017/12/1

N2 - Background: In the last decade Photogrammetry has shown to be a valid alternative to LiDAR techniques for the generation of dense point clouds in many applications. However, dealing with large image sets is computationally demanding. It requires high performance hardware and often long processing times that makes the photogrammetric point cloud generation not suitable for mapping purposes at regional and national scale. These limitations are partially overcome by commercial solutions, thanks to the use of expensive and dedicated hardware. Nonetheless, a Free and Open-Source Software (FOSS) photogrammetric solution able to cope with these limitations is still missing.Methods: In this paper, the bottlenecks of the basic components of photogrammetric workflows -tie-points extraction, bundle block adjustment (BBA) and dense image matching- are tackled implementing FOSS solutions. We present distributed computing algorithms for the tie-points extraction and for the dense image matching. Moreover, we present two algorithms for decreasing the memory needs of the BBA. The various algorithms are deployed on different hardware systems including a computer cluster.Results and conclusions: The usage of the algorithms presented allows to process large image sets reducing the computational time. This is demonstrated using two different datasets.

AB - Background: In the last decade Photogrammetry has shown to be a valid alternative to LiDAR techniques for the generation of dense point clouds in many applications. However, dealing with large image sets is computationally demanding. It requires high performance hardware and often long processing times that makes the photogrammetric point cloud generation not suitable for mapping purposes at regional and national scale. These limitations are partially overcome by commercial solutions, thanks to the use of expensive and dedicated hardware. Nonetheless, a Free and Open-Source Software (FOSS) photogrammetric solution able to cope with these limitations is still missing.Methods: In this paper, the bottlenecks of the basic components of photogrammetric workflows -tie-points extraction, bundle block adjustment (BBA) and dense image matching- are tackled implementing FOSS solutions. We present distributed computing algorithms for the tie-points extraction and for the dense image matching. Moreover, we present two algorithms for decreasing the memory needs of the BBA. The various algorithms are deployed on different hardware systems including a computer cluster.Results and conclusions: The usage of the algorithms presented allows to process large image sets reducing the computational time. This is demonstrated using two different datasets.

KW - ITC-GOLD

UR - http://ezproxy.utwente.nl:2048/login?url=https://webapps.itc.utwente.nl/library/2017/ref/nex_imp.pdf

U2 - 10.1186/s40965-017-0024-5

DO - 10.1186/s40965-017-0024-5

M3 - Article

VL - 2

JO - Open Geospatial Data, Software and Standards

JF - Open Geospatial Data, Software and Standards

SN - 2363-7501

M1 - 12

ER -