一种基于UAV和Sentinel-2的新型图像融合方法,用于环境监测
Fan Zhang1, Aobo Guo2, Zhenqi Hu3
1School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
Scientific reports
|July 27, 2025
概括
一种新的融合方法结合了无人机和Sentinel-2图像,用于在采矿区进行详细的环境监测. 这种方法通过整合重新采样和堆叠学习模型来显著提高准确性.
科学领域:
- 环境科学 环境科学
- 遥感技术 遥感技术 遥感技术
- 地理空间分析是什么
背景情况:
- 卫星遥感图像缺乏足够的分辨率来详细监测小规模采矿区.
- 无人机遥感提供了高分辨率,但范围有限,缺乏历史数据.
- 在采矿区监测中,将多个来源数据与不同的时空特征融合在一起,在方法上是具有挑战性的.
研究的目的:
- 开发一种新的融合方法,用于采矿区的小规模,长期环境监测.
- 解决个人遥感数据源 (无人机和卫星) 的局限性.
- 准确捕捉采矿环境中复杂的动态变化.
主要方法:
- 将无人机和哨兵-2图像重新采样到0.1米的空间分辨率.
- 为了提高数据质量,采用双层预处理方法.
- 使用集体学习框架开发一个堆叠的倒置模型.
主要成果:
- 堆叠学习模型与立方卷积重新抽样相结合,将NDVI值的平均绝对百分比误差 (MAPE) 从54.31%降至10.01%.
- 准确性评估使用高分辨率无人机图像作为参考和Sentinel-2图像 (原始,重新采样,模型倒置) 作为实验值.
- 在改进数据准确性方面,重新采样技术和模型架构之间表现出协同效应.
结论:
- 拟议的融合方法为采矿区的小规模,长期环境监测提供了可靠的数据支持.
- 该研究强调了将先进的建模和数据融合技术整合到有效的环境监测中的重要性.
- 这些发现有助于克服在动态采矿环境中利用多源遥感数据的挑战.
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