Bark Texture Classification Using Deep Transfer Learning

Bhusnurmath, Rohini A.; Doddamani, Shaila

doi:10.1007/978-3-031-36402-0_38

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-36402-0_38

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Bark Texture Classification Using Deep Transfer Learning

Rohini A. Bhusnurmath,

Shaila Doddamani

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2024

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Cited by 2 publications

References 23 publications

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Classification of Tree Species Using Point Cloud Features from Terrestrial Laser Scanning

Meng,

Dong,

Han

et al. 2024

Forests

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The automatic classification of tree species using terrestrial laser scanning (TLS) point clouds is key in forestry research. This study aims to develop a robust framework for tree species classification by integrating advanced feature extraction and machine learning techniques. Such a framework is of great significance for investigating and monitoring forest resources, sustainable forest management, and biodiversity research. To achieve this, point cloud data from 360 trees of four species were collected at the Northeastern Forestry University in Harbin City, Heilongjiang Province. Three types of tree point cloud features were extracted: tree structure, bark texture, and bark color. In addition, to repair and optimize the bark point cloud data, improved bark texture features were generated using the kriging interpolation method. These four features were combined into seven classification schemes and input into a random forest classifier, which was used to accurately classify the tree species. The results showed that the classification scheme combining tree structure features, improved bark texture features, and bark color features performed the best, with an overall classification accuracy of 94.17% and a kappa coefficient of 0.92. This study highlights the effectiveness of integrating point cloud data with machine learning algorithms for tree species classification and proposes a feature extraction and classification framework that significantly enhances classification accuracy.

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Classification of Tree Species Using Point Cloud Features from Terrestrial Laser Scanning

Meng,

Dong,

Han

et al. 2024

Forests

View full text Add to dashboard Cite

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Enhancing bark texture analysis and species classification with deep learning models: Wide residual networks and convNeXt

Bhusnurmath,

Doddamani

2024

Indian J. Forest.

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This study delves into the analysis of bark texture images using deep learning methods to efficiently classify different wood species. With applications spanning from construction to furniture manufacturing, efficient and precise wood species classification is vital for effective forestry management and the timber trade. The research centers on a dataset featuring images of 50 distinct wood species, each characterized by unique texture patterns. Two deep learning models, Wide Residual Networks (WRN) and ConvNeXt, are employed and compared for their analysis purposes. Results consistently demonstrate WRN's superior performance, attributed to its architectural design and effective training strategy in capturing intricate texture patterns. Notably, WRN achieves impressive efficiency alongside high accuracy, precision, and recall rates of 97.23%, 97.29%, and 97.23%, respectively. WRN's success over the pre-processed dataset underscores its versatility and robustness in handling complex texture patterns. Overall, the study showcases the transformative potential of deep learning in revolutionizing tree species classification.

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Bark Texture Classification Using Deep Transfer Learning

Cited by 2 publications

References 23 publications

Classification of Tree Species Using Point Cloud Features from Terrestrial Laser Scanning

Classification of Tree Species Using Point Cloud Features from Terrestrial Laser Scanning

Enhancing bark texture analysis and species classification with deep learning models: Wide residual networks and convNeXt

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