Tree species recognition system based on macroscopic image analysis

Ibrahim, Ibrahim Amer; Khairuddin, Anis Salwa Mohd; Talip, Mohamad Sofıan Abu; Arof, Hamzah; Yusof, Rubiyah

doi:10.1007/s00226-016-0859-4

Cited by 32 publications

(31 citation statements)

References 18 publications

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“…e microscopic hyperspectral images of cross sections mainly include wood vessels, wood rays, and axial parenchyma, as shown in Figure 1. e closed holes are wood vessels which are important features for species recognition [12,13]. e dense, thin, approximately parallel lines are called wood rays, while the thick lines approximately perpendicular to the wood rays are the axial parenchyma.…”

Section: Spectral Analysis Of Different Tissues Of Hardwood Microscopmentioning

confidence: 99%

Hardwood Species Classification with Hyperspectral Microscopic Images

Zhao

Wang

2019

Journal of Spectroscopy

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We propose a hardwood species identification method based on wood hyperspectral microscopic images. A SOC710VP hyperspectral stereomicroscope was used to acquire microscopic images of a hardwood cross section. In these microscopic images, each part’s spectral features are discussed. We found that the spectral divisibility of wood vessels’ peripheral and central regions in the hyperspectral microscopic images can be used for hardwood species recognition. Mathematical morphological operation and K-L divergence were used to extract spectral features at the wood vessels’ peripheral regions and central regions, respectively. By comparing wood vessels’ spectral similarity across wood species samples, we found that wood vessels’ peripheral spectral divisibility is larger than its central. Finally, the spectral information from randomly selected regions of interest (i.e., ROI) and that of wood vessels’ peripheral and central regions have value as a classification basis. In our hardwood species classification experiments, three dimensionality reduction algorithms, principal component analysis (PCA), kernel principal component analysis (KPCA), and multidimensional scaling (MDS), and the three classifiers, BP neural network, support vector machine (SVM), and Mahalanobis distance (MD), are combined to perform hardwood species classification work. Experimental results indicate that the best recognition effect can be achieved at the peripheral region of wood vessels using PCA or MDS with the MD algorithm.

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Section: Spectral Analysis Of Different Tissues Of Hardwood Microscopmentioning

confidence: 99%

Hardwood Species Classification with Hyperspectral Microscopic Images

Zhao

Wang

2019

Journal of Spectroscopy

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“…Wood species are distinctively identified by the physical aspects of the tree, such as the trunk shape, leaves and flowers (Ibrahim et al, 2017). When wood alone is available, the analysis assumes greater complexity, requiring time and knowledgeable specialists and needs to be performed based on the macro-and microscopic characteristics (Oliveira et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Among the first group, the works of Piuri & Scotti (2010), Oliveira et al (2015), and Nisgoski et al (2017a, b) rank high. Among the image-analysis studies, those of Khalid et al (2008), Wang et al (2013), Yusof et al (2013), Paula et al (2014), Martins et al (2015), Zamri et al (2016), and Ibrahim et al (2017) are eminent. Both techniques revealed acceptable results.…”

Section: Introductionmentioning

confidence: 99%

Potential of Texture Analysis for Charcoal Classification

et al. 2019

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Charcoal produced from reforested wood can be distinguished from the charcoal derived from the wood of native species. This identification is very important for the trade, control and monitoring of charcoal production in Brazil. This study investigated the potential of texture analysis for classifying the charcoal based on origin (eucalyptus or native) and species. A total of 17 wood species were studied, five of which belonged to genus Eucalyptus and 12 were native to the Zona da Mata Mineira. Texture features based on the gray level co-occurrence matrix were extracted from digital images. The linear discriminant analysis was used to classify the images with these features. Employing 10 features, 96.2% accuracy was achieved for the classification by origin and 90.4% for the categorization by species. Texture analysis was shown to be a favorable and effective method that could facilitate the establishment of semiautomated techniques to classify the charcoal based on origin or species.

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“…For example, Yusof et al proposed a kernel-genetic nonlinear feature selection scheme for 52 tropical wood species recognition [8]. Ibrahim et al proposed a fuzzy preclassifier to classify 48 tropical wood species into 4 broad categories, and then they used a Support Vector Machine (SVM) classifier in each broad category to further determine wood species of the detected wood sample [9]. ey pointed out that one advantage is that when a new wood species is added into the system, only the SVM classifier of one broad category requires to be retrained instead of the whole system.…”

Section: Introductionmentioning

confidence: 99%

“…ey pointed out that one advantage is that when a new wood species is added into the system, only the SVM classifier of one broad category requires to be retrained instead of the whole system. But this two-level classification system may not recognize the unknown new wood species automatically [9].…”

Section: Introductionmentioning

confidence: 99%

An Incremental Self-Adaptive Wood Species Classification Prototype System

Zhao

2019

Journal of Spectroscopy

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The present wood species classification systems can usually process the limited wood species quantity. We propose a novel incremental self-adaptive wood species classification system to solve the above-mentioned issue. The visible/near-infrared (VIS/NIR) spectrometer is used to pick up the spectral curves of wood samples for the subsequent wood species classification. First, when new wood samples of unknown wood species are added, they are classified as an unknown category by our one-class classifier, Support Vector Data Description (SVDD), while the existent wood species are classified as a known category by the SVDD. Second, the wood samples of known species are sent into the BP neural network for subsequent wood species classification. Third, the new wood samples of unknown species are sent into the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm for the unsupervised clustering, and the clustering result is evaluated by the internal and external norms. Last, if one cluster of one unknown species has an adequate amount of wood samples, these wood samples are removed and identified by human experts or other schemes to ensure to get the correct wood species name. Then, these wood samples are considered as a new known species and are sent into the classifiers, SVDD and BP neural network, to train them again. Experiments on 13 wood species prove the effectiveness of our prototype system with an overall classification accuracy of above 95%.

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Tree species recognition system based on macroscopic image analysis

Cited by 32 publications

References 18 publications

Hardwood Species Classification with Hyperspectral Microscopic Images

Hardwood Species Classification with Hyperspectral Microscopic Images

Potential of Texture Analysis for Charcoal Classification

An Incremental Self-Adaptive Wood Species Classification Prototype System

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