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

Alanoud Al Mazroa1, Nuha Alruwais2, Muhammad Kashif Saeed3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a new method for classifying aerial images from Unmanned Aerial Vehicle (UAV) networks using deep learning and a Snake Optimization Algorithm. The approach achieves 99.75% accuracy, significantly improving aerial image classification.

Keywords:
Deep LearningDenseNetImage classificationSnake Optimization AlgorithmUnmanned Aerial Vehicle

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Multi-class aerial image classification (AIC) is vital for Unmanned Aerial Vehicle (UAV) networks in applications like environmental monitoring and infrastructure inspection.
  • Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), excel at analyzing complex aerial imagery by extracting spectral and spatial features.
  • Optimizing DL models is crucial for enhancing the accuracy of AIC in UAV systems.

Purpose of the Study:

  • To propose a novel methodology, Snake Optimization Algorithm with Deep Learning for Multi-Class Aerial Image Classification (SOADL-MCAIC), for UAV networks.
  • To enhance the accuracy and efficiency of multi-class aerial image classification using advanced AI techniques.
  • To develop a robust system for recognizing diverse classes within aerial imagery captured by UAVs.

Main Methods:

  • The SOADL-MCAIC methodology employs Gaussian filtering (GF) for image pre-processing.
  • An Efficient DenseNet model is utilized for learning intricate features from aerial images.
  • Snake Optimization Algorithm (SOA) is applied for hyperparameter tuning of the Efficient DenseNet model.
  • Kernel Extreme Learning Machine (KELM) is implemented for the final classification of aerial images.

Main Results:

  • The SOADL-MCAIC method achieved a superior classification accuracy of 99.75% on the UCM land use dataset.
  • The proposed method demonstrated significant improvements over existing models in multi-class aerial image classification.
  • The integration of SOA for hyperparameter tuning effectively enhanced the performance of the Efficient DenseNet model.

Conclusions:

  • The SOADL-MCAIC methodology provides a highly accurate and effective solution for multi-class aerial image classification in UAV networks.
  • This research contributes to the advancement of autonomous aerial systems by improving surveillance, reconnaissance, and remote sensing capabilities.
  • The proposed approach highlights the potential of combining optimization algorithms with deep learning for complex image analysis tasks.