Multi class aerial image classification in UAV networks employing Snake Optimization Algorithm with Deep Learning.

Journal: Scientific reports
Published Date: Jul 4, 2025

Abstract

In Unmanned Aerial Vehicle (UAV) networks, multi-class aerial image classification (AIC) is crucial in various applications, from environmental monitoring to infrastructure inspection. Deep Learning (DL), a powerful tool in artificial intelligence (AI), proves significant in this context, enabling the model to analyze and classify complex aerial images effectually. By utilizing advanced neural network architectures, such as convolutional neural networks (CNN), DL models outperform at identifying complex features and patterns within the aerial imagery. These models can extract spectral and spatial information from the captured data, classifying diverse terrains, structures, and objects precisely. Furthermore, the integration of Snake Optimization algorithms assists in fine-tuning the classification process, improving accuracy. As UAV networks continue to expand, DL-powered multi-class AIC significantly enhances the performance of surveillance, reconnaissance, and remote sensing tasks, contributing to the advancement of autonomous aerial systems. This study proposes a Snake Optimization Algorithm with Deep Learning for Multi-Class Aerial Image Classification (SOADL-MCAIC) methodology on UAV Networks. The main purpose of SOADL-MCAIC methodology is to recognize the presence of multiple classes of aerial images on the UAV networks. To accomplish this, the SOADL-MCAIC technique utilizes Gaussian filtering (GF) for pre-processing. In addition, the SOADL-MCAIC technique employs the Efficient DenseNet model to learn difficult and intrinsic features in the image. The SOA-based hyperparameter tuning process is used to enhance the performance of the Efficient DenseNet technique. Finally, the kernel extreme learning machine (KELM)-based classification algorithm is implemented to identify and classify the presence of various classes in aerial images. The simulation outcomes of the SOADL-MCAIC method are examined under the UCM land use dataset. The experimental analysis of the SOADL-MCAIC method portrayed a superior accuracy value of 99.75% over existing models.

Authors

  • Alanoud Al Mazroa
    Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia. asalmazroa@pnu.edu.sa.
  • Nuha Alruwais
    Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, Riyadh 11495, Saudi Arabia.
  • Muhammad Kashif Saeed
    Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia.
  • Kamal M Othman
    Department of Electrical Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, Saudi Arabia.
  • Randa Allafi
    Department of Computers and Information Technology, College of Sciences and Arts, Northern Border University, Arar, Saudi Arabia.
  • Ahmed S Salama
    Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt.

Keywords

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