使用多源数据进行土壤碳含量预测的功能是基于光谱和超光谱图像的深度学习的融合
Xueying Li1, Zongmin Li2, Huimin Qiu3
1Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266061, China; College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266590, China.
Chemosphere
|June 11, 2023
概括
这项研究融合了可见近红外反射光谱 (VNIR) 和高光谱图像 (HSI) 数据,以改善土壤碳含量预测. 将这些源与人工特征相结合,可显著提高准确性和稳定性.
科学领域:
- 地球和环境科学 地球和环境科学
- 遥感 遥感 遥感 遥感
- 土壤科学 土壤科学
背景情况:
- 可见近红外反射光谱 (VNIR) 和高光谱图像 (HSI) 为土壤碳含量预测提供了互补的优势.
- 当前的方法往往缺乏在多源数据融合中对特征贡献的深入分析.
- 对比人工特征与深度学习特征用于土壤碳预测的研究有限.
研究的目的:
- 开发和评估使用融合VNIR和HSI多源数据进行土壤碳含量预测的新方法.
- 分析各种特征的贡献差异,包括人工和深度学习衍生的特征.
- 通过有效的数据融合策略,提高土壤碳预测的准确性和稳定性.
主要方法:
- 设计了一个多源数据融合网络,利用注意力机制来权衡功能贡献.
- 开发了一种替代的聚变网络,在光谱数据旁边结合了人工特征.
- 使用来自尼鲁,阿山湾和州湾的数据评估了预测准确性.
主要成果:
- 基于注意力的聚变网络提高了土壤碳预测的准确性.
- 结合人工特征的融合网络展示了卓越的预测性能.
- 与单一来源数据相比,相对百分比偏差的减少是显著的,在不同位置有显著的改善.
结论:
- 多源数据融合,特别是具有人工特征的数据融合,有效地解决了用于土壤碳预测的深度特征融合的挑战.
- 提出的方法提高了预测的准确性和稳定性,促进了VNIR和HSI在土壤碳监测中的应用.
- 这项研究为碳循环研究和碳汇估计提供了技术支持.
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