TY  - JOUR
AU  - Drobnjak, Siniša
AU  - Stojanović, Marko
AU  - Djordjević, Dejan
AU  - Bakrač, Saša
AU  - Jovanović, Jasmina
AU  - Djordjević, Aleksandar
PY  - 2022
UR  - http://gery.gef.bg.ac.rs/handle/123456789/1415
AB  - The objective of this research is to report results from a new ensemble method for
vegetation classification that uses deep learning (DL) and machine learning (ML)
techniques. Deep learning and machine learning architectures have recently been used
in methods for vegetation classification, proving their efficacy in several scientific
investigations. However, some limitations have been highlighted in the literature, such
as insufficient model variance and restricted generalization capabilities. Ensemble DL and
ML models has often been recommended as a feasible method to overcome these
constraints. A considerable increase in classification accuracy for vegetation classification
was achieved by growing an ensemble of decision trees and allowing them to vote for the
most popular class. An ensemble DL and ML architecture is presented in this study to
increase the prediction capability of individual DL and ML models. Three DL and ML
models, namely Convolutional Neural Network (CNN), Random Forest (RF), and biased
Support vector machine (B-SVM), are used to classify vegetation in the Eastern part of
Serbia, together with their ensemble form (CNN-RF-BSVM). The suggested DL and ML
ensemble architecture achieved the best modeling results with overall accuracy values
(0.93), followed by CNN (0.90), RF (0.91), and B-SVM (0.88). The results showed that the
suggested ensemble model outperformed the DL and ML models in terms of overall
accuracy by up to 5%, which was validated by the Wilcoxon signed-rank test. According to
this research, RF classifiers require fewer and easier-to-define user-defined parameters
than B-SVMs and CNN methods. According to overall accuracy analysis, the proposed
ensemble technique CNN-RF-BSVM also significantly improved classification
accuracy (by 4%).
PB  - Frontiers Media SA
T2  - Frontiers in Environmental Science
T1  - Testing a New Ensemble Vegetation Classification Method Based on Deep Learning and Machine Learning Methods Using Aerial Photogrammetric Images
VL  - 10
SP  - 896158
DO  - 10.3389/fenvs.2022.896158
ER  - 

@article{
author = "Drobnjak, Siniša and Stojanović, Marko and Djordjević, Dejan and Bakrač, Saša and Jovanović, Jasmina and Djordjević, Aleksandar",
year = "2022",
abstract = "The objective of this research is to report results from a new ensemble method for
vegetation classification that uses deep learning (DL) and machine learning (ML)
techniques. Deep learning and machine learning architectures have recently been used
in methods for vegetation classification, proving their efficacy in several scientific
investigations. However, some limitations have been highlighted in the literature, such
as insufficient model variance and restricted generalization capabilities. Ensemble DL and
ML models has often been recommended as a feasible method to overcome these
constraints. A considerable increase in classification accuracy for vegetation classification
was achieved by growing an ensemble of decision trees and allowing them to vote for the
most popular class. An ensemble DL and ML architecture is presented in this study to
increase the prediction capability of individual DL and ML models. Three DL and ML
models, namely Convolutional Neural Network (CNN), Random Forest (RF), and biased
Support vector machine (B-SVM), are used to classify vegetation in the Eastern part of
Serbia, together with their ensemble form (CNN-RF-BSVM). The suggested DL and ML
ensemble architecture achieved the best modeling results with overall accuracy values
(0.93), followed by CNN (0.90), RF (0.91), and B-SVM (0.88). The results showed that the
suggested ensemble model outperformed the DL and ML models in terms of overall
accuracy by up to 5%, which was validated by the Wilcoxon signed-rank test. According to
this research, RF classifiers require fewer and easier-to-define user-defined parameters
than B-SVMs and CNN methods. According to overall accuracy analysis, the proposed
ensemble technique CNN-RF-BSVM also significantly improved classification
accuracy (by 4%).",
publisher = "Frontiers Media SA",
journal = "Frontiers in Environmental Science",
title = "Testing a New Ensemble Vegetation Classification Method Based on Deep Learning and Machine Learning Methods Using Aerial Photogrammetric Images",
volume = "10",
pages = "896158",
doi = "10.3389/fenvs.2022.896158"
}

Drobnjak, S., Stojanović, M., Djordjević, D., Bakrač, S., Jovanović, J.,& Djordjević, A.. (2022). Testing a New Ensemble Vegetation Classification Method Based on Deep Learning and Machine Learning Methods Using Aerial Photogrammetric Images. in Frontiers in Environmental Science
Frontiers Media SA., 10, 896158.
https://doi.org/10.3389/fenvs.2022.896158

Drobnjak S, Stojanović M, Djordjević D, Bakrač S, Jovanović J, Djordjević A. Testing a New Ensemble Vegetation Classification Method Based on Deep Learning and Machine Learning Methods Using Aerial Photogrammetric Images. in Frontiers in Environmental Science. 2022;10:896158.
doi:10.3389/fenvs.2022.896158 .

Drobnjak, Siniša, Stojanović, Marko, Djordjević, Dejan, Bakrač, Saša, Jovanović, Jasmina, Djordjević, Aleksandar, "Testing a New Ensemble Vegetation Classification Method Based on Deep Learning and Machine Learning Methods Using Aerial Photogrammetric Images" in Frontiers in Environmental Science, 10 (2022):896158,
https://doi.org/10.3389/fenvs.2022.896158 . .