一个有效的保护隐私的基于多层融合的特征工程框架,用于使用遥感图像进行无人机支持的土地覆盖分类
S Nagadevi1, G Abirami1, R Brindha1
1Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India.
Scientific reports
|July 3, 2025
概括
本研究引入了无人机网络的新型入侵检测模型,提高了遥感应用中的土地覆盖分类准确性. 该模型实现了高性能,在动态环境中确保安全和高效的数据处理.
科学领域:
- 遥感 遥感 遥感 遥感
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 在陆地覆盖分类等应用中比载人飞机提供了显著的优势.
- 网络无人机系统容易受到恶意攻击,需要强大的入侵检测系统 (IDS).
- 深度学习 (DL) 对于解决无人机网络的安全挑战至关重要.
研究的目的:
- 为基于无人机的遥感应用在使用多层融合特征工程 (IDUAVRS-LCCMFFE) 的土地覆盖分类中提出有效的隐私保护入侵检测模型.
- 通过在动态环境中使用无人机图像来提高土地覆盖分类的准确性和安全性.
主要方法:
- 使用联合双边过器 (JBF) 进行图像预处理,以减少噪声.
- 通过NASNetMobile,ResNet50和VGG19模型的融合进行多级特征提取.
- 使用Elman循环神经网络 (ERNN) 进行土地覆盖分类,并通过Salp Swarm Algorithm (SSA) 进行优化.
主要成果:
- IDUAVRS-LCCMFFE模型实现了卓越的准确性,在ToN-IoT数据集上达到99.66%.
- 欧卫星数据集的验证也显示出高性能,准确度为96.47%.
- 拟议的技术有效地提高了基于无人机的遥感的图像质量和分类准确性.
结论:
- IDUAVRS-LCCMFFE模型提供了一个强大的解决方案,用于在无人机网络中的入侵检测,用于陆地覆盖的分类.
- 多层融合特征工程方法显著提高了分类性能.
- 这项研究有助于确保基于无人机的遥感应用.
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