Classification of energy tree species using support vector machines

Burai, Péter; Bekő, László; Lénárt, Csaba; Tomor, Tamás

doi:10.1109/whispers.2014.8077499

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…The blend of traffic rate examination and flooding attacks discovery component empowers Internet assets to be sheltered and stable from the continuous flooding attacks. An Adaptive parallel tree SVM (ABSVM) classifier was created in (Burai, P., Beko, L., Lenart, C., & Tomor, T, 2014) agreement with the standard of SVM. This paper explores characterization strategies (MLC, SVM) utilizing highlight extraction can segregate among species and clones of vitality trees.…”

Section: Related Workmentioning

confidence: 99%

Detection Mechanism Using Transductive Learning and Support Vectors for Software-Defined Networks

Kaur

Gupta

Kumar

2022

International Journal of Information Retrieval Research

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SDN has come up as a promising technology for a future network as a logically centralized controlled framework along with its physically distributed architecture isolating the control plane from sending data moving the entire choice capacity to the regulator. SDNs are turning out to be significant because of scalability, adaptability and testing. As SDN needs overhead for operation, it makes it as a target of Distributed Denial of service (DDoS) attacks. The extensive review in the existing literature survey provides results for small footprint of dataset causing over fitting of the classifier. In the survey it is also been observed that the KNN based algorithms to detect DDOS attacks are lazy learners resulting in the noisy data. This paper proposes a Dual Probability Transductive Confidence Machines and Support Vector Machine (DPTCM-SVM) classifier to avoid the over-fitting for detecting DDoS in SDN. The results generated for detection are more than 98% for all the attack classes making it an Eager Learning System which requires less learning space unlike the Lazy Learning Systems.

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Section: Related Workmentioning

confidence: 99%

Detection Mechanism Using Transductive Learning and Support Vectors for Software-Defined Networks

Kaur

Gupta

Kumar

2022

International Journal of Information Retrieval Research

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“…Burai et al 2019-es kutatásában egy úgynevezett objektumalapú osztályozási módszert dolgozott ki, mely pontosabb és átláthatóbb fafajtérképet eredményezett. Burai et al 2014-…”

Section: Bevezetésunclassified

Légi hiperspektrális felvétel osztályozási pontosságának vizsgálata fás mintaterületen

Likó¹,

Bekő²,

Burai³

et al. 2021

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“…Conventional research methods for tree species classification include manual feature extraction and classical machine learning algorithms, such as Support Vector Machines (SVMs) [5][6][7], Artificial Neural Networks (ANNs) [8,9], and Random Forest (RF) [10][11][12]. Burai, P. et al [13] used airborne HS imagery and image classification methods (multi-label classification and SVM) combined with feature extraction to discriminate between species and clones of energy trees. They proposed an adaptive binary tree SVM classifier (ABTSVM) to improve the species-level classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Tree Species Classification in UAV Remote Sensing Images Based on Super-Resolution Reconstruction and Deep Learning

et al. 2023

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We studied the use of self-attention mechanism networks (SAN) and convolutional neural networks (CNNs) for forest tree species classification using unmanned aerial vehicle (UAV) remote sensing imagery in Dongtai Forest Farm, Jiangsu Province, China. We trained and validated representative CNN models, such as ResNet and ConvNeXt, as well as the SAN model, which incorporates Transformer models such as Swin Transformer and Vision Transformer (ViT). Our goal was to compare and evaluate the performance and accuracy of these networks when used in parallel. Due to various factors, such as noise, motion blur, and atmospheric scattering, the quality of low-altitude aerial images may be compromised, resulting in indistinct tree crown edges and deficient texture. To address these issues, we adopted Real-ESRGAN technology for image super-resolution reconstruction. Our results showed that the image dataset after reconstruction improved classification accuracy for both the CNN and Transformer models. The final classification accuracies, validated by ResNet, ConvNeXt, ViT, and Swin Transformer, were 96.71%, 98.70%, 97.88%, and 98.59%, respectively, with corresponding improvements of 1.39%, 1.53%, 0.47%, and 1.18%. Our study highlights the potential benefits of Transformer and CNN for forest tree species classification and the importance of addressing the image quality degradation issues in low-altitude aerial images.

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Classification of energy tree species using support vector machines

Cited by 4 publications

References 7 publications

Detection Mechanism Using Transductive Learning and Support Vectors for Software-Defined Networks

Detection Mechanism Using Transductive Learning and Support Vectors for Software-Defined Networks

Légi hiperspektrális felvétel osztályozási pontosságának vizsgálata fás mintaterületen

Tree Species Classification in UAV Remote Sensing Images Based on Super-Resolution Reconstruction and Deep Learning

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