Published

2024-09-19

Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification

Investigación sobre el desempeño de máquinas de vectores de soporte, bosque aleatorio y redes neuronales convolucionales 3D y 2D en la clasificación de imágenes hiperespectrales

DOI:

https://doi.org/10.15446/esrj.v28n2.105296

Keywords:

hyperspectral image classification, support vector machine, random forest, convolutional neural networks, Houston 2013, HyRANK, Salinas Scene (en)
clasificación de imágenes hiperespectrales, máquinas de vectores de soporte, bosque aleatorio, redes neuronales convolucionales, Houston 2013, HyRANK, Salinas Scene (es)

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Classification of the hyperspectral images (HSIs) is one of the most challenging tasks hyperspectral remote sensing. Various Machine Learning classification algorithms have been implemented to HSI classification. In recent years, several Convolutional Neural Network (CNN) architectures were developed for HSI classification. The aim of this study is to test the performance of CNN, and well-known Support Vector Machine and Random Forest algorithms using the HyRANK Loukia, Houston 2013, and Salinas Scene datasets. The findings indicate that the Modified HybridSN CNN outperformed other algorithms across all datasets, as demonstrated by various performance evaluation metrics.

La clasificación de imágenes hiperespectrales (HSI, del inglés hyperspectral images) es una de las tareas más complejas de la detección remota hiperespectral. Varios algoritmos de aprendizaje de máquinas se han implementado en la clasificación de las HSI. Recientemente, varias arquitecturas basadas en redes neuronales convolucionales (CNN, del inglés Convolutional Neural Networks) se han desarrollado para esta clasificación de imágenes hiperespectrales. El objetivo de este estudio es evaluar el desempeño de las CNN y los algoritmos de máquinas de vectores de soporte y de bosque aleatorio con los conjuntos de datos HyRANK Loukia, Houston 2013 y Salinas Scene. Los resultados demuestran que el modelo Modified HybridSN CNN superó  a otros algoritmos en todos los conjuntos de datos de acuerdo con lo que demuestran varias métricas de evaluación de desempeño.

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How to Cite

APA

Seyrek, E. C. and Uysal, M. (2024). Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification. Earth Sciences Research Journal, 28(2), 161–174. https://doi.org/10.15446/esrj.v28n2.105296

ACM

[1]
Seyrek, E.C. and Uysal, M. 2024. Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification. Earth Sciences Research Journal. 28, 2 (Sep. 2024), 161–174. DOI:https://doi.org/10.15446/esrj.v28n2.105296.

ACS

(1)
Seyrek, E. C.; Uysal, M. Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification. Earth sci. res. j. 2024, 28, 161-174.

ABNT

SEYREK, E. C.; UYSAL, M. Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification. Earth Sciences Research Journal, [S. l.], v. 28, n. 2, p. 161–174, 2024. DOI: 10.15446/esrj.v28n2.105296. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/105296. Acesso em: 22 apr. 2025.

Chicago

Seyrek, Eren Can, and Murat Uysal. 2024. “Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification”. Earth Sciences Research Journal 28 (2):161-74. https://doi.org/10.15446/esrj.v28n2.105296.

Harvard

Seyrek, E. C. and Uysal, M. (2024) “Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification”, Earth Sciences Research Journal, 28(2), pp. 161–174. doi: 10.15446/esrj.v28n2.105296.

IEEE

[1]
E. C. Seyrek and M. Uysal, “Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification”, Earth sci. res. j., vol. 28, no. 2, pp. 161–174, Sep. 2024.

MLA

Seyrek, E. C., and M. Uysal. “Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification”. Earth Sciences Research Journal, vol. 28, no. 2, Sept. 2024, pp. 161-74, doi:10.15446/esrj.v28n2.105296.

Turabian

Seyrek, Eren Can, and Murat Uysal. “Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification”. Earth Sciences Research Journal 28, no. 2 (September 19, 2024): 161–174. Accessed April 22, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/105296.

Vancouver

1.
Seyrek EC, Uysal M. Investigation of the performances of Support Vector Machine, Random Forest, and 3D-2D Convolutional Neural Network for Hyperspectral Image Classification. Earth sci. res. j. [Internet]. 2024 Sep. 19 [cited 2025 Apr. 22];28(2):161-74. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/105296

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