NDLI: Automated classification of wood transverse cross-section micro-imagery from 77 commercial Central-African timber species

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Automated classification of wood transverse cross-section micro-imagery from 77 commercial Central-African timber species

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Annals of Forest Science : Volume 74, Issue 1, March 2017

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Automated classification of wood transverse cross-section micro-imagery from 77 commercial Central-African timber species

Content Provider	Springer Nature Link
Author	da Silva, Núbia De Ridder, Maaike Baetens, Jan M. Van den Bulcke, Jan Rousseau, Mélissa Bruno, Odemir Beeckman, Hans Van Acker, Joris De Baets, Bernard
Copyright Year	2017
Abstract	Pattern recognition has become an important tool to aid in the identification and classification of timber species. In this context, the focus of this work is the classification of wood species using texture characteristics of transverse cross-section images obtained by microscopy. The results show that this approach is robust and promising. Considering the lack of automated methods for wood species classification, machine vision based on pattern recognition might offer a feasible and attractive solution because it is less dependent on expert knowledge, while existing databases containing high-quality microscopy images can be exploited.This work focuses on the automated classification of 1221 micro-images originating from 77 commercial timber species from the Democratic Republic of Congo.Microscopic images of transverse cross-sections of all wood species are taken in a standardized way using a magnification of 25 ×. The images are represented as texture feature vectors extracted using local phase quantization or local binary patterns and classified by a nearest neighbor classifier according to a triplet of labels (species, genus, family).The classification combining both local phase quantization and linear discriminant analysis results in an average success rate of approximately 88% at species level, 89% at genus level and 90% at family level. The success rate of the classification method is remarkably high. More than 50% of the species are never misclassified or only once. The success rate is increasing from the species, over the genus to the family level. Quite often, pattern recognition results can be explained anatomically. Species with a high success rate show diagnostic features in the images used, whereas species with a low success rate often have distinctive anatomical features at other microscopic magnifications or orientations than those used in our approach.This work demonstrates the potential of a semi-automated classification by resorting to pattern recognition. Semi-automated systems like this could become valuable tools complementing conventional wood identification.
Starting Page	1
Ending Page	14
Page Count	14
File Format	PDF
ISSN	12864560
Journal	Annals of Forest Science
Volume Number	74
Issue Number	2
e-ISSN	1297966X
Language	English
Publisher	Springer Paris
Publisher Date	2017-04-03
Publisher Institution	Institut National de la Recherche Agronomique (INRA)
Publisher Place	Paris
Access Restriction	Subscribed
Subject Keyword	Commercial timber species Democratic Republic of Congo Image analysis Pattern recognition Transverse cross-section Wood anatomy Forestry Wood Science & Technology Forestry Management Tree Biology Environment
Content Type	Text
Resource Type	Article
Subject	Ecology Forestry

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