Al Mazroa Alanoud, Alruwais Nuha, Saeed Muhammad Kashif, Othman Kamal M, Allafi Randa, Salama Ahmed S
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, 11495, Riyadh, Saudi Arabia.
Sci Rep. 2025 Jul 4;15(1):23872. doi: 10.1038/s41598-025-04570-8.
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.
在无人机（UAV）网络中，多类航空图像分类（AIC）在从环境监测到基础设施检查等各种应用中至关重要。深度学习（DL）作为人工智能（AI）中的强大工具，在这种情况下证明具有重要意义，使模型能够有效地分析和分类复杂的航空图像。通过利用先进的神经网络架构，如卷积神经网络（CNN），DL模型在识别航空图像中的复杂特征和模式方面表现出色。这些模型可以从捕获的数据中提取光谱和空间信息，精确地对不同的地形、结构和物体进行分类。此外，蛇优化算法的集成有助于微调分类过程，提高准确性。随着无人机网络的不断扩展，由DL驱动的多类AIC显著提高了监视、侦察和遥感任务的性能，推动了自主航空系统的发展。本研究提出了一种用于无人机网络的基于深度学习的蛇优化算法多类航空图像分类（SOADL - MCAIC）方法。SOADL - MCAIC方法的主要目的是识别无人机网络上多类航空图像的存在。为实现这一目标，SOADL - MCAIC技术利用高斯滤波（GF）进行预处理。此外，SOADL - MCAIC技术采用高效密集连接网络（Efficient DenseNet）模型来学习图像中困难和内在的特征。基于SOA的超参数调整过程用于提高Efficient DenseNet技术的性能。最后，实施基于核极限学习机（KELM）的分类算法来识别和分类航空图像中各类别的存在。在UCM土地利用数据集下对SOADL - MCAIC方法的仿真结果进行了检验。SOADL - MCAIC方法的实验分析表明，其精度值比现有模型高出99.75%。
