Früher|Nächster

Research progress and challenges of data-driven quantitative remote sensing

  • role： First author第一作者
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079

  • Email：qianqian_yang@whu.edu.cn
  • Introduction：杨倩倩，1995年生，女，博士研究生，研究方向为大气遥感。E-mail： qianqian_yang@whu.edu.cn
YANG Qianqian1,  
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079

JIN Caiyi1,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082

LI Tongwen2,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079

  • Email：qqyuan@sgg.whu.edu.cn
  • Introduction：袁强强，1985年生，男，教授，研究方向为遥感信息处理，定量遥感，机器学习。E-mail： qqyuan@sgg.whu.edu.cn
YUAN Qiangqiang1*,  
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079

SHEN Huanfeng3,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079

ZHANG Liangpei4

Resümee

Quantitative remote sensing is a technique for quantitatively inferring or inverting earth environmental variable from the original remote sensing observations, it is an important step to turn electromagnetic wave signals into easy-to-understand information about surface environment. Traditional quantitative remote sensing methods are mainly model-driven, emphasizing inversion through mathematical or physical models. With the development and popularization of artificial intelligence technology, data-driven methods have gradually received widespread attention. It emphasizes the use of machine learning methods to mine the information contained in remote sensing observation data to achieve the quantitative inversion of geophysical parameters. With the support of powerful computing capacity, the data-driven method has achieved gratifying achievements in many fields of quantitative remote sensing. This article systematically summarizes the principles, characteristics, and applications in quantitative remote sensing field of different types of data-driven models, including regression algorithms, regularization methods, instance-based algorithms, decision tree, Bayesian methods, kernel based algorithms, genetic algorithms, ensemble learning, artificial neural network, and deep learning. Though data-driven models show satisfying retrieval performance in multiple fields, its drawbacks of ignoring the laws of physics and lack of causality have also brought resistance to its development. In this context, coupling the laws of physics and machine learning to develop an inversion framework driven by both models and data has become a new research hotspot. Some pioneering researches have already achieved delightful performance through using machine learning to assist physical models or restricting machine learning with physical laws. Using machine learning techniques to optimize the systematic basis, the sub-model, and the model parameters largely improve the performance of model-driven methods. Meanwhile, integrating physics knowledge into machine learning models through adjusting the training data, modifying the loss function, and constraining the solution space also benefit the improvement of data-driven models. However, there are still great challenges to be broken through. Physical models contain complex mechanisms and rich knowledge, current fusion of data-driven and model-driven methods are quite shallow with very limited amount of physical knowledge being used. A deeper coupling strategy is worth exploring in the future. Besides, the uncertainty, generalization, and transferability of the joint model have not been scientifically evaluated currently, to which attention should be paid. Finally, there are many cases when the training samples were very difficult to obtain, therefore, the applicability of the joint model in the case of small samples is also a problem that needs to be solved urgently. The deep, robust, and generalizable coupling of data-driven and model-driven models is expected in the future.

Schlüsselwort

remote sensing;quantitative remote sensing;model-driven;data-driven;deep learning;machine learning

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