Prairie Dog Optimization Algorithm with deep learning assisted based Aerial Image Classification on UAV imagery

• Published in: Heliyon Vol. 10; no. 18; p. e37446
• Main Authors: K. Alkhalifa, Amal, Kashif Saeed, Muhammad, M. Othman, Kamal, A. Ebad, Shouki, Alonazi, Mohammed, Mohamed, Abdullah
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 30.09.2024; Elsevier
• Subjects: Aerial image classification; Deep learning; Prairie dog optimization; Remote sensing; UAV; Deep learning; Prairie dog optimization; Aerial image classification; UAV; Remote sensing
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e37446

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.