Deep learning in environmental remote sensing: Achievements and challenges

Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of “big data” from earth observation and rapid advances in ML, increasing opportunities for novel methods have emerged to aid in earth environme...

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Veröffentlicht in:Remote sensing of environment Jg. 241; S. 111716
Hauptverfasser: Yuan, Qiangqiang, Shen, Huanfeng, Li, Tongwen, Li, Zhiwei, Li, Shuwen, Jiang, Yun, Xu, Hongzhang, Tan, Weiwei, Yang, Qianqian, Wang, Jiwen, Gao, Jianhao, Zhang, Liangpei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Inc 01.05.2020
Elsevier BV
Schlagworte:
air
Air temperature
artificial intelligence
Atmospheric models
color
Data integration
Deep learning
Earth
environmental factors
Environmental monitoring
Environmental remote sensing
Evapotranspiration
Hydrology
Land cover
Land surface temperature
Learning algorithms
Machine learning
Mapping
Multisensor fusion
Neural network
Neural networks
Ocean color
Parameter retrieval
prediction
Remote sensing
Solar radiation
Surface temperature
vegetation
Deep learning
Environmental remote sensing
Neural network
Parameter retrieval
ISSN:0034-4257, 1879-0704
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Zusammenfassung:Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of “big data” from earth observation and rapid advances in ML, increasing opportunities for novel methods have emerged to aid in earth environmental monitoring. Over the last decade, a typical and state-of-the-art ML framework named deep learning (DL), which is developed from the traditional neural network (NN), has outperformed traditional models with considerable improvement in performance. Substantial progress in developing a DL methodology for a variety of earth science applications has been observed. Therefore, this review will concentrate on the use of the traditional NN and DL methods to advance the environmental remote sensing process. First, the potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed. A typical network structure will then be introduced. Afterward, the applications of DL environmental monitoring in the atmosphere, vegetation, hydrology, air and land surface temperature, evapotranspiration, solar radiation, and ocean color are specifically reviewed. Finally, challenges and future perspectives will be comprehensively analyzed and discussed. •The potential of deep learning (DL) in environmental remote sensing is analyzed.•Typical DL network architectures in remote sensing applications are introduced.•Progress on DL in remote sensing of ten more environmental parameters is reviewed.•New insights on combining DL and physical/geographical laws are discussed.
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ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2020.111716