Statistical feature extraction method for wood species recognition system

Ibrahim, Ibrahim Amer; Khairuddin, Anis Salwa Mohd; Arof, Hamzah; Yusof, Rubiyah; Hanafi, Effariza

doi:10.1007/s00107-017-1163-1

Cited by 23 publications

(14 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hyperspectral image classification (HSI) of wood categories, e.g., heartwood and sapwood, is a relatively unexplored research area. Current research [2,4,3] have tried to solve this problem in conventional ways: first, by extracting the features, and, then, by applying classical classifiers to estimate the wood categories. However, to the best of our knowledge, In this paper, we proposed a CNN-based hyperspectral image spatial classifier, which neither requires a large dataset, nor specific resources in terms of GPU or memory.…”

Section: Discussionmentioning

confidence: 99%

“…First, representative features are extracted, and, then, a model is trained. Techniques to perform heartwood-sapwood distinction amount to extract features using PCA [2] or statistical properties of the wood texture [3] in both spatial and spectral dimensions, and then by classifying them using neural networks [4,2,3]. Recently, other approaches have exploited Deep Learning for HSI classification in the agriculture domain [5,6].…”

Section: Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

A Lightweight Approach For Wood Hyperspectral Images Classification

Htun

Boschetti²,

Buriro

et al. 2021

2021 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

View full text Add to dashboard Cite

This paper presents a Convolutional Neural Network (CNN)based spatial classifier to classify hyperspectral images for wood recognition. The spatial classifier is built by adapting the input and output units of Cifar10Net, a conventional image classifier that accepts three-band images as input. Obtained results in terms of accuracy and training time show that the proposed classifier can be trained using few training data, and few computational resources.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

A Lightweight Approach For Wood Hyperspectral Images Classification

Htun

Boschetti²,

Buriro

et al. 2021

2021 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

View full text Add to dashboard Cite

show abstract

“…At the end of the last century and the beginning of this century, the traditional identification work is slow and costly, and the experts need to find up many special features through using the microscope to identify the cross section of the wood [3]. These works are usually done by experts manually.…”

Section: Problem Definition and Related Workmentioning

confidence: 99%

Wood Species Recognition with Small Data: A Deep Learning Approach

Sun¹,

Lin²,

He³

et al. 2021

IJCIS

View full text Add to dashboard Cite

Wood species recognition is an important work in the wood trade and wood commercial activities. Although many recognition methods were presented in recent years, the existing wood species recognition methods mainly use shallow recognition models with low accuracy and are still unsatisfying for many real-world applications. Besides, their generalization ability is not strong. In this paper, a novel deep-learning-based wood species recognition method was proposed, which improved the accuracy and generalization greatly. The method uses 20X amplifying glass to acquire wood images, extracts the image features with ResNet50 neural network, refines the features with linear discriminant analysis (LDA), and recognizes the wood species with a KNN classifier. Our data was small, but we adopted transfer learning to improve our method. About 3000 wood images were used in our wood species recognition experiments and our method was executed in 25 rare wood species and the results showed our method had better generalization performance and accuracy. Compared with traditional deep learning our results were obtained from a small amount of data, which just confirmed the effectiveness of our method.

show abstract

“…In these state-of-the-art wood species recognition schemes, Yusof et al employed texture feature operators (e.g., basic gray-level aura matrix (BGLAM), improved basic gray-level aura matrix (I-BGLAM)), and pore distribution features (i.e., statistical properties of pores distribution (SPPD)) and several feature extraction algorithms, such as the genetic algorithm (GA) and kernel discriminant analysis (KDA) to classify more than 50 tropical wood species [18,19,35,37]. However, their schemes were only suitable for the classification of hardwood species with pores.…”

Section: Comparisons With State-of-the-art Algorithmsmentioning

confidence: 99%

Wood Species Recognition Based on Visible and Near-Infrared Spectral Analysis Using Fuzzy Reasoning and Decision-Level Fusion

Zhao

Wang

2021

Journal of Spectroscopy

View full text Add to dashboard Cite

A novel wood species spectral classification scheme is proposed based on a fuzzy rule classifier. The visible/near-infrared (VIS/NIR) spectral reflectance curve of a wood sample’s cross section was captured using a USB 2000-VIS-NIR spectrometer and a FLAME-NIR spectrometer. First, the wood spectral curve—with spectral bands of 376.64–779.84 nm and 950–1650 nm—was processed using the principal component analysis (PCA) dimension reduction algorithm. The wood spectral data were divided into two datasets, namely, training and testing sets. The training set was used to generate the membership functions and the initial fuzzy rule set, with the fuzzy rule being adjusted to supplement and refine the classification rules to form a perfect fuzzy rule set. Second, a fuzzy classifier was applied to the VIS and NIR bands. An improved decision-level fusion scheme based on the Dempster–Shafer (D-S) evidential theory was proposed to further improve the accuracy of wood species recognition. The test results using the testing set indicated that the overall recognition accuracy (ORA) of our scheme reached 94.76% for 50 wood species, which is superior to that of conventional classification algorithms and recent state-of-the-art wood species classification schemes. This method can rapidly achieve good recognition results, especially using small datasets, owing to its low computational time and space complexity.

show abstract

Statistical feature extraction method for wood species recognition system

Cited by 23 publications

References 16 publications

A Lightweight Approach For Wood Hyperspectral Images Classification

A Lightweight Approach For Wood Hyperspectral Images Classification

Wood Species Recognition with Small Data: A Deep Learning Approach

Wood Species Recognition Based on Visible and Near-Infrared Spectral Analysis Using Fuzzy Reasoning and Decision-Level Fusion

Contact Info

Product

Resources

About