Prairie Dog Optimization Algorithm with deep learning assisted based Aerial Image Classification on UAV imagery
Amal K. Alkhalifa,
Muhammad Kashif Saeed,
Kamal M. Othman,
Shouki A. Ebad,
Mohammed Alonazi,
Abdullah Mohamed
Affiliations
Amal K. Alkhalifa
Department of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
Muhammad Kashif Saeed
Department of Computer Science, Applied College at Mahayil, King Khalid University, Saudi Arabia
Kamal M. Othman
Department of Electrical Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, Saudi Arabia
Shouki A. Ebad
Department of Computer Science, Faculty of Science, Northern Border University, Arar, 91431, Saudi Arabia; Corresponding author.
Mohammed Alonazi
Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia
Abdullah Mohamed
Research Centre, Future University in Egypt, New Cairo, 11845, Egypt
This study presents a Prairie Dog Optimization Algorithm with a Deep learning-assisted Aerial Image Classification Approach (PDODL-AICA) on UAV images. The PDODL-AICA technique exploits the optimal DL model for classifying aerial images into numerous classes. In the presented PDODL-AICA technique, the feature extraction procedure is executed using the EfficientNetB7 model. Besides, the hyperparameter tuning of the EfficientNetB7 technique uses the PDO model. The PDODL-AICA technique uses a convolutional variational autoencoder (CVAE) model to detect and classify aerial images. The performance study of the PDODL-AICA model is implemented on a benchmark UAV image dataset. The experimental values inferred the authority of the PDODL-AICA approach over recent models in terms of dissimilar measures.
