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Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix

Classifying wood species accurately is crucial since incorrect labelling of wood species may incur huge loss to timber industries. An automated wood species recognition system is designed based on image analysis of the wood texture which consists of image acquisition, feature extraction, and classif...

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Main Authors:	Zamri, M.I.P., Cordova, F., Khairuddin, A.S.M., Mokhtar, N., Yusof, R.
Format:	Article
Published:	Elsevier 2016
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/18690/ https://doi.org/10.1016/j.compag.2016.04.004
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spelling	my.um.eprints.186902018-05-16T02:53:23Z http://eprints.um.edu.my/18690/ Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix Zamri, M.I.P. Cordova, F. Khairuddin, A.S.M. Mokhtar, N. Yusof, R. TK Electrical engineering. Electronics Nuclear engineering Classifying wood species accurately is crucial since incorrect labelling of wood species may incur huge loss to timber industries. An automated wood species recognition system is designed based on image analysis of the wood texture which consists of image acquisition, feature extraction, and classification. There are 100 images captured from each wood sample which are divided into training samples and testing samples. An effective feature extractor is important to extract most discriminant features from the wood texture in order to distinguish the wood species accurately. Therefore, in this paper, a novel feature extractor based on Improved-Basic Gray Level Aura Matrix (I-BGLAM) technique is proposed to extract 136 features from each wood image. Fundamentally, the proposed I-BGLAM feature extractor which focuses on the gray level of the wood images is rotational invariant and has smaller feature dimension since only discriminative features are considered. Then, the proposed system automatically classifies 52 wood species by using backpropagation neural network classifier. The proposed I-BGLAM feature extractor had shown to overcome the limitations of Gray Level Co-occurrence Matrix (GLCM) and conventional BGLAM feature extractors in wood species recognition system. Experiments were performed to determine which dataset would be the most ideal when dividing the 100 wood images into training samples and testing samples. Results showed that the most ideal dataset that should be used is dataset that consists of 80 training samples and 20 test samples. The proposed method showed marked improvement of 97.01% accuracy to the work done previously. Elsevier 2016 Article PeerReviewed Zamri, M.I.P. and Cordova, F. and Khairuddin, A.S.M. and Mokhtar, N. and Yusof, R. (2016) Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix. Computers and Electronics in Agriculture, 124. pp. 227-233. ISSN 0168-1699 https://doi.org/10.1016/j.compag.2016.04.004 doi:10.1016/j.compag.2016.04.004
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Zamri, M.I.P. Cordova, F. Khairuddin, A.S.M. Mokhtar, N. Yusof, R. Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix
description	Classifying wood species accurately is crucial since incorrect labelling of wood species may incur huge loss to timber industries. An automated wood species recognition system is designed based on image analysis of the wood texture which consists of image acquisition, feature extraction, and classification. There are 100 images captured from each wood sample which are divided into training samples and testing samples. An effective feature extractor is important to extract most discriminant features from the wood texture in order to distinguish the wood species accurately. Therefore, in this paper, a novel feature extractor based on Improved-Basic Gray Level Aura Matrix (I-BGLAM) technique is proposed to extract 136 features from each wood image. Fundamentally, the proposed I-BGLAM feature extractor which focuses on the gray level of the wood images is rotational invariant and has smaller feature dimension since only discriminative features are considered. Then, the proposed system automatically classifies 52 wood species by using backpropagation neural network classifier. The proposed I-BGLAM feature extractor had shown to overcome the limitations of Gray Level Co-occurrence Matrix (GLCM) and conventional BGLAM feature extractors in wood species recognition system. Experiments were performed to determine which dataset would be the most ideal when dividing the 100 wood images into training samples and testing samples. Results showed that the most ideal dataset that should be used is dataset that consists of 80 training samples and 20 test samples. The proposed method showed marked improvement of 97.01% accuracy to the work done previously.
format	Article
author	Zamri, M.I.P. Cordova, F. Khairuddin, A.S.M. Mokhtar, N. Yusof, R.
author_facet	Zamri, M.I.P. Cordova, F. Khairuddin, A.S.M. Mokhtar, N. Yusof, R.
author_sort	Zamri, M.I.P.
title	Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix
title_short	Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix
title_full	Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix
title_fullStr	Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix
title_full_unstemmed	Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix
title_sort	tree species classification based on image analysis using improved-basic gray level aura matrix
publisher	Elsevier
publishDate	2016
url	http://eprints.um.edu.my/18690/ https://doi.org/10.1016/j.compag.2016.04.004
_version_	1643690767176171520
score	13.211869

Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix

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