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Classification of terrain using superpixel segmentation and supervised learning
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Klassificering av terräng med superpixelsegmentering och övervakad inlärning (Swedish)
Abstract [en]

The usage of 3D-modeling is expanding rapidly. Modeling from aerial imagery has become very popular due to its increasing number of both civilian and mili- tary applications like urban planning, navigation and target acquisition.

This master thesis project was carried out at Vricon Systems at SAAB. The Vricon system produces high resolution geospatial 3D data based on aerial imagery from manned aircrafts, unmanned aerial vehicles (UAV) and satellites.

The aim of this work was to investigate to what degree superpixel segmentation and supervised learning can be applied to a terrain classification problem using imagery and digital surface models (dsm). The aim was also to investigate how the height information from the digital surface model may contribute compared to the information from the grayscale values. The goal was to identify buildings, trees and ground. Another task was to evaluate existing methods, and compare results.

The approach for solving the stated goal was divided into several parts. The first part was to segment the image using superpixel segmentation, after that features were extracted. Then the classifiers were created and trained and finally the classifiers were evaluated.

The classification method that obtained the best results in this thesis had approx- imately 90 % correctly labeled superpixels. The result was equal, if not better, compared to other solutions available on the market. 

Place, publisher, year, edition, pages
2014. , 96 p.
Keyword [en]
Segmentation, Superpixels, Features, Classification, Machine learning, Random forest
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-112511ISRN: LiTH-ISY-EX--14/4752--SEOAI: oai:DiVA.org:liu-112511DiVA: diva2:767120
External cooperation
SAAB Vricon Systems AB
Subject / course
Computer Vision Laboratory
Supervisors
Examiners
Available from: 2014-12-01 Created: 2014-11-30 Last updated: 2014-12-01Bibliographically approved

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CiteExportLink to record
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Citation style
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