Published

2025-02-13

Classification of Remote Sensing Datasets with Different Deep Learning Architectures

Clasificación de grupos de datos de detección remota con diferentes arquitecturas de aprendizaje profundo

DOI:

https://doi.org/10.15446/esrj.v28n4.113518

Keywords:

Convolutional Neural Networks (CNN), AlexNet, Resnet-50, VGG16, Efficient-Net-B0, Remote Sensing (RS) Image Classification, AID, AIDER, Unmanned Aerial Vehicles (UAVs) (en)
Redes Neuronales Convolucionales (CNN), AlexNet, Resnet-50, VGG16, Efficient-Net-B0, clasificación de imágenes de detección remota, grupo de datos AID, grupo de datos AIDER, vehículos aéreos no tripulados (es)

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Authors

  • Maryam Mehmood Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
  • Farhan Hussain Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
  • Ahsan Shahzad Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
  • Nouman Ali Department of Software Engineering, Mirpur University of Science and Technology

Remote sensing image classification has great advantages in the areas of environmental monitoring, urban planning, disaster management and many others. Unmanned Aerial Vehicles (UAVs) have revolutionized remote sensing by providing high-resolution imagery. In this context, effective image classification is crucial for extracting meaningful information from UAV-captured images. This study presents a comparison of different deep learning-based approach for supervised image classification of UAV images. We have experimented on four different CNN models like VGG 16, Alex net, Resnet50 and a deep neural network Efficient-Net-B0 on different remote sensing datasets; AID and AIDER. Multiple combinations were tried to find out which model performs better on which type of datasets. We have used pre-trained initial layers of four CNN models (AlexNet, VGG 16, Resnet50 and Efficient-Net-Bo) then last three layers of each of the selected models are removed and new layers have been added with better tuned parameters. Two different schemes were analyzed. In Scheme-1 the original AlexNet, VGG 16, Resnet50 and Efficient-Net-B0 were experimented without changing and tuning their number of parameters, while in Scheme-2 transfer learning was applied on the pre-trained models and after removing last three layers new layers were added with better tuned hyper-parameters. The evaluation of above schemes was ensured through comprehensive metrics across diverse land cover classes, four different performance evaluation matrices namely; F1 score, precision, accuracy and recall. The main focus of this research is towards transfer learning and adding new layers into pre-trained models to get better classification accuracy.

La clasificación de imágenes de detección remota tiene grandes ventajas en las áreas de monitoreo ambiental, planeación urbana, manejo de desastres y muchos otros. Los vehículos aéreos no tripulados han revolucionizado la detección remota al proveer imágenes de alta resolución. En este contexto, la clasificación efectiva de imágenes es crucial para extraer información significativa de las imágenes capturadas por vehículos aéreos no tripulados. Este estudio presenta una comparación de diferentes técnicas de aprendizaje profundo para la clasificación supervisada de imágenes capturadas por vehículos aéreos no tripulados. Los autores experimentaron con diferentes grupos de datos AID y AIDER en cuatro modelos diferentes de Redes Neuronales Convolucionales (CNN), VGG 16, Alex net, Resnet50 y en la red neuronal profunda Efficient-Net-B0. Se intentaron múltiples combinaciones para encontrar el modelo con mayor desempeño en cada grupo de datos. Los autores usaron capas iniciales de preentrenamiento de los modelos CNN y luego se retiraron las tres últimas capas de cada uno de los modelos seleccionados para añadir luego capas con parámetros más acordes. Se analizaron dos esquemas diferentes. En el Esquema 1 se experimentaron los modelos CNN originales sin cambiar y sin adecuar el número de parámetros, mientras que en el Esquema 2 se aplicó la transferencia de aprendizaje en los modelos pre-entrenados y después de remover las tres últimas capas se añadieron nuevas capas con hiperparámetros más adecuados. La evaluación de estos esquemas fue asegurada a través de métricas completas para diversas clases de cobertura del suelo y con cuatro matrices de evaluación de desempeño llamadas puntuación F1, precisión, exactitud y exhaustividad. El foco principal de esta investigación se basa en la transferencia de aprendizaje y en la adición de nuevas capas en modelos pre-entrenados para tener una clasificación más precisa.

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How to Cite

APA

Mehmood, M., Hussain, F., Shahzad, A. and Ali, N. (2025). Classification of Remote Sensing Datasets with Different Deep Learning Architectures. Earth Sciences Research Journal, 28(4), 409–419. https://doi.org/10.15446/esrj.v28n4.113518

ACM

[1]
Mehmood, M., Hussain, F., Shahzad, A. and Ali, N. 2025. Classification of Remote Sensing Datasets with Different Deep Learning Architectures. Earth Sciences Research Journal. 28, 4 (Feb. 2025), 409–419. DOI:https://doi.org/10.15446/esrj.v28n4.113518.

ACS

(1)
Mehmood, M.; Hussain, F.; Shahzad, A.; Ali, N. Classification of Remote Sensing Datasets with Different Deep Learning Architectures. Earth sci. res. j. 2025, 28, 409-419.

ABNT

MEHMOOD, M.; HUSSAIN, F.; SHAHZAD, A.; ALI, N. Classification of Remote Sensing Datasets with Different Deep Learning Architectures. Earth Sciences Research Journal, [S. l.], v. 28, n. 4, p. 409–419, 2025. DOI: 10.15446/esrj.v28n4.113518. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/113518. Acesso em: 19 apr. 2025.

Chicago

Mehmood, Maryam, Farhan Hussain, Ahsan Shahzad, and Nouman Ali. 2025. “Classification of Remote Sensing Datasets with Different Deep Learning Architectures”. Earth Sciences Research Journal 28 (4):409-19. https://doi.org/10.15446/esrj.v28n4.113518.

Harvard

Mehmood, M., Hussain, F., Shahzad, A. and Ali, N. (2025) “Classification of Remote Sensing Datasets with Different Deep Learning Architectures”, Earth Sciences Research Journal, 28(4), pp. 409–419. doi: 10.15446/esrj.v28n4.113518.

IEEE

[1]
M. Mehmood, F. Hussain, A. Shahzad, and N. Ali, “Classification of Remote Sensing Datasets with Different Deep Learning Architectures”, Earth sci. res. j., vol. 28, no. 4, pp. 409–419, Feb. 2025.

MLA

Mehmood, M., F. Hussain, A. Shahzad, and N. Ali. “Classification of Remote Sensing Datasets with Different Deep Learning Architectures”. Earth Sciences Research Journal, vol. 28, no. 4, Feb. 2025, pp. 409-1, doi:10.15446/esrj.v28n4.113518.

Turabian

Mehmood, Maryam, Farhan Hussain, Ahsan Shahzad, and Nouman Ali. “Classification of Remote Sensing Datasets with Different Deep Learning Architectures”. Earth Sciences Research Journal 28, no. 4 (February 13, 2025): 409–419. Accessed April 19, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/113518.

Vancouver

1.
Mehmood M, Hussain F, Shahzad A, Ali N. Classification of Remote Sensing Datasets with Different Deep Learning Architectures. Earth sci. res. j. [Internet]. 2025 Feb. 13 [cited 2025 Apr. 19];28(4):409-1. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/113518

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