混合学习框架,用于SAR和光学无人机数据在野火监测中的协同融合
Balaji Ganesh Rajagopal1, K S Monish1, Deebalakshmi R1
1SRM Institute of Science and Technology Tiruchirappalli, Tiruchirappalli, India.
Scientific reports
|November 29, 2025
概括
这项研究介绍了一种混合人工智能系统,用于使用雷达和无人机图像检测野火. 先进的框架提供了改进的早期检测和监测,增强灾害响应和生态系统的弹性.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 野火构成了重大的全球威胁,需要先进的早期检测和监测解决方案.
- 现有的系统经常在全天候监视和精确的火线划分方面扎.
研究的目的:
- 为加强野火早期检测和监测引入一种全新的多式联运人工智能框架.
- 整合合成孔径雷达 (SAR) 和无人机 (UAV) 图像以进行全面的监视.
- 开发一个能够为灾难应对提供可操作见解的系统.
主要方法:
- 一个混合学习框架,结合了FPANet (基于视觉变压器) 的SAR数据分割和DualSegFormer用于无人机热和RGB图像的多模式融合.
- 整合视觉语言模型 (VLM) 以将复杂的传感器数据转化为人类可读的洞察力.
- 利用广域SAR进行全天候监视和高分辨率无人机图像进行精确分析.
主要成果:
- 在基于SAR的细分方面,FPANet获得了F1得分0.830,IOU为0.750.
- 在基于无人机的火线前线划分方面,DualSegFormer表现出高的F1得分0.946.
- VLM组件取得了0.953的BERTScore,这表明有很强的语义对齐.
结论:
- 与传统方法相比,拟议的混合学习方法显著提高了野火监测能力.
- 综合系统提供了更有效和可靠的野火检测,对于生态系统的弹性至关重要.
- 这一进步支持及时应对灾害,并与国际可持续发展目标 (SDG) 保持一致.
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