Change detection between digital surface models from airborne laser scanning and dense image matching using convolutional neural networks

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Abstract

Airborne photogrammetry and airborne laser scanning are two commonly used technologies used for topographical data acquisition at the city level. Change detection between airborne laser scanning data and photogrammetric data is challenging since the two point clouds show different characteristics. After comparing the two types of point clouds, this paper proposes a feed-forward Convolutional Neural Network (CNN) to detect building changes between them. The motivation from an application point of view is that the multimodal point clouds might be available for different epochs. Our method contains three steps: First, the point clouds and orthoimages are converted to raster images. Second, square patches are cropped from raster images and then fed into CNN for change detection. Finally, the original change map is post-processed with a simple connected component analysis. Experimental results show that the patch-based recall rate reaches 0.8146 and the precision rate reaches 0.7632. Object-based evaluation shows that 74 out of 86 building changes are correctly detected.
Original languageEnglish
Title of host publicationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Subtitle of host publicationISPRS Geospatial Week 2019
EditorsG. Vosselman, S.J. Oude Elberink, M.Y. Wang
Place of PublicationEnschede
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages453-460
Number of pages8
VolumeIV
Edition2/W5
DOIs
Publication statusPublished - 29 May 2019
Event4th ISPRS Geospatial Week 2019 - University of Twente, Enschede, Netherlands
Duration: 10 Jun 201914 Jun 2019
Conference number: 4
https://www.gsw2019.org/

Publication series

NameISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherCopernicus
ISSN (Print)2194-9042

Conference

Conference4th ISPRS Geospatial Week 2019
CountryNetherlands
CityEnschede
Period10/06/1914/06/19
Internet address

Fingerprint

airborne lasers
change detection
Image matching
raster
laser
Neural networks
Scanning
scanning
Photogrammetry
Lasers
photogrammetry
data acquisition
Data acquisition
time measurement
detection
rate
evaluation
lasers
method
city

Keywords

  • ITC-GOLD
  • Digital Surface Model (DSM)
  • Change Detection
  • Airborne Laser Scanning
  • Dense Image Matching
  • Convolutional Neural Network (CNN)

Cite this

Zhang, Z., Vosselman, G., Gerke, M., Persello, C., Tuia, D., & Yang, M. Y. (2019). Change detection between digital surface models from airborne laser scanning and dense image matching using convolutional neural networks. In G. Vosselman, S. J. Oude Elberink, & M. Y. Wang (Eds.), ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS Geospatial Week 2019 (2/W5 ed., Vol. IV, pp. 453-460). (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences). Enschede: International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprs-annals-IV-2-W5-453-2019
Zhang, Z. ; Vosselman, G. ; Gerke, M. ; Persello, C. ; Tuia, D. ; Yang, M. Y. / Change detection between digital surface models from airborne laser scanning and dense image matching using convolutional neural networks. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS Geospatial Week 2019. editor / G. Vosselman ; S.J. Oude Elberink ; M.Y. Wang. Vol. IV 2/W5. ed. Enschede : International Society for Photogrammetry and Remote Sensing (ISPRS), 2019. pp. 453-460 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences).
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abstract = "Airborne photogrammetry and airborne laser scanning are two commonly used technologies used for topographical data acquisition at the city level. Change detection between airborne laser scanning data and photogrammetric data is challenging since the two point clouds show different characteristics. After comparing the two types of point clouds, this paper proposes a feed-forward Convolutional Neural Network (CNN) to detect building changes between them. The motivation from an application point of view is that the multimodal point clouds might be available for different epochs. Our method contains three steps: First, the point clouds and orthoimages are converted to raster images. Second, square patches are cropped from raster images and then fed into CNN for change detection. Finally, the original change map is post-processed with a simple connected component analysis. Experimental results show that the patch-based recall rate reaches 0.8146 and the precision rate reaches 0.7632. Object-based evaluation shows that 74 out of 86 building changes are correctly detected.",
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Zhang, Z, Vosselman, G, Gerke, M, Persello, C, Tuia, D & Yang, MY 2019, Change detection between digital surface models from airborne laser scanning and dense image matching using convolutional neural networks. in G Vosselman, SJ Oude Elberink & MY Wang (eds), ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS Geospatial Week 2019. 2/W5 edn, vol. IV, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, International Society for Photogrammetry and Remote Sensing (ISPRS), Enschede, pp. 453-460, 4th ISPRS Geospatial Week 2019, Enschede, Netherlands, 10/06/19. https://doi.org/10.5194/isprs-annals-IV-2-W5-453-2019

Change detection between digital surface models from airborne laser scanning and dense image matching using convolutional neural networks. / Zhang, Z.; Vosselman, G.; Gerke, M.; Persello, C.; Tuia, D.; Yang, M. Y.

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS Geospatial Week 2019. ed. / G. Vosselman; S.J. Oude Elberink; M.Y. Wang. Vol. IV 2/W5. ed. Enschede : International Society for Photogrammetry and Remote Sensing (ISPRS), 2019. p. 453-460 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

TY - CHAP

T1 - Change detection between digital surface models from airborne laser scanning and dense image matching using convolutional neural networks

AU - Zhang, Z.

AU - Vosselman, G.

AU - Gerke, M.

AU - Persello, C.

AU - Tuia, D.

AU - Yang, M. Y.

PY - 2019/5/29

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N2 - Airborne photogrammetry and airborne laser scanning are two commonly used technologies used for topographical data acquisition at the city level. Change detection between airborne laser scanning data and photogrammetric data is challenging since the two point clouds show different characteristics. After comparing the two types of point clouds, this paper proposes a feed-forward Convolutional Neural Network (CNN) to detect building changes between them. The motivation from an application point of view is that the multimodal point clouds might be available for different epochs. Our method contains three steps: First, the point clouds and orthoimages are converted to raster images. Second, square patches are cropped from raster images and then fed into CNN for change detection. Finally, the original change map is post-processed with a simple connected component analysis. Experimental results show that the patch-based recall rate reaches 0.8146 and the precision rate reaches 0.7632. Object-based evaluation shows that 74 out of 86 building changes are correctly detected.

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KW - Airborne Laser Scanning

KW - Dense Image Matching

KW - Convolutional Neural Network (CNN)

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Zhang Z, Vosselman G, Gerke M, Persello C, Tuia D, Yang MY. Change detection between digital surface models from airborne laser scanning and dense image matching using convolutional neural networks. In Vosselman G, Oude Elberink SJ, Wang MY, editors, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS Geospatial Week 2019. 2/W5 ed. Vol. IV. Enschede: International Society for Photogrammetry and Remote Sensing (ISPRS). 2019. p. 453-460. (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences). https://doi.org/10.5194/isprs-annals-IV-2-W5-453-2019