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Photometric Methods for Autonomous Tree Species Classification and NIR Quality Inspection
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this paper the brief overview of methods available for individual tree stems quality evaluation and tree species classification has been performed. The use of Near Infrared photometry based on conifer’s canopy reflectance measurement in near infrared range of spectrum has been evaluated for the use in autonomous forest harvesting. Photometric method based on the image processing of the bark pattern has been proposed to perform classification between main construction timber tree species in Scandinavia: Norway spruce (Picea abies) and Scots Pine (Pinus sylvestris). Several feature extraction algorithms have been evaluated, resulting two methods selected: Statistical Analysis using gray level co-occurrence matrix and maximally stable extremal regions feature detector. Feedforward Neural Network with Backpropagation training algorithm and Support Vector Machine classifiers have been implemented and compared. The verification of the proposed algorithm has been performed by real-time testing.

Place, publisher, year, edition, pages
2015. , 75 p.
, MMK 2015:54 MKN 142
Keyword [en]
RGB imaging, NIR imaging, Artificial Neural Networks, Tree species classification, Gray Level Co-occurrence Matrix, Maximally Stable Extremal Regions
National Category
Mechanical Engineering
URN: urn:nbn:se:kth:diva-176266OAI: diva2:866213
Available from: 2015-11-03 Created: 2015-11-02 Last updated: 2015-11-03Bibliographically approved

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