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Evaluation of Aerial Image Stereo Matching Methods for Forest Variable Estimation
Linköping University, Department of Electrical Engineering, Computer Vision.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This work investigates the landscape of aerial image stereo matching (AISM) methods suitable for large scale forest variable estimation. AISM methods are an important source of remotely collected information used in modern forestry to keep track of a growing forest's condition.

A total of 17 AISM methods are investigated, out of which 4 are evaluated by processing a test data set consisting of three aerial images. The test area is located in southern Sweden, consisting of mainly Norway Spruce and Scots Pine. From the resulting point clouds and height raster images, a total of 30 different metrics of both height and density types are derived. Linear regression is used to fit functions from metrics derived from AISM data to a set of forest variables including tree height (HBW), tree diameter (DBW), basal area, volume. As ground truth, data collected by dense airborne laser scanning is used. Results are presented as RMSE and standard deviation concluded from the linear regression.

For tree height, tree diameter, basal area, volume the RMSE ranged from 7.442% to 10.11%, 11.58% to 13.96%, 32.01% to 35.10% and 34.01% to 38.26% respectively. The results concluded that all four tested methods achieved comparable estimation quality although showing small differences among them. Keystone and SURE performed somewhat better while MicMac placed third and Photoscan achieved the less accurate result.

Place, publisher, year, edition, pages
2017. , p. 68
Keywords [en]
aerial image stereo matching evaluation forest variable estimation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-138166ISRN: LiTH-ISY-EX--17/5036--SEOAI: oai:DiVA.org:liu-138166DiVA, id: diva2:1109735
External cooperation
Foran Sverige AB
Subject / course
Computer Vision Laboratory
Presentation
2017-05-16, Algoritmen, 15:15 (Swedish)
Supervisors
Examiners
Available from: 2017-06-19 Created: 2017-06-14 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf