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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| Published in | Remote sensing of environment Vol. 241; p. 111716 |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Elsevier Inc
01.05.2020
Elsevier BV |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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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| AbstractList | 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. 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. |
| ArticleNumber | 111716 |
| Author | Yuan, Qiangqiang Gao, Jianhao Tan, Weiwei Yang, Qianqian Zhang, Liangpei Wang, Jiwen Li, Zhiwei Shen, Huanfeng Jiang, Yun Li, Tongwen Xu, Hongzhang Li, Shuwen |
| Author_xml | – sequence: 1 givenname: Qiangqiang orcidid: 0000-0001-7140-2224 surname: Yuan fullname: Yuan, Qiangqiang organization: School of Geodesy and Geomatics, Wuhan University, Wuhan, China – sequence: 2 givenname: Huanfeng orcidid: 0000-0002-4140-1869 surname: Shen fullname: Shen, Huanfeng email: shenhf@whu.edu.cn organization: School of Resource and Environmental Sciences, Wuhan University, Wuhan, China – sequence: 3 givenname: Tongwen surname: Li fullname: Li, Tongwen organization: School of Resource and Environmental Sciences, Wuhan University, Wuhan, China – sequence: 4 givenname: Zhiwei orcidid: 0000-0001-5635-8499 surname: Li fullname: Li, Zhiwei organization: School of Resource and Environmental Sciences, Wuhan University, Wuhan, China – sequence: 5 givenname: Shuwen surname: Li fullname: Li, Shuwen organization: School of Geodesy and Geomatics, Wuhan University, Wuhan, China – sequence: 6 givenname: Yun surname: Jiang fullname: Jiang, Yun organization: School of Resource and Environmental Sciences, Wuhan University, Wuhan, China – sequence: 7 givenname: Hongzhang surname: Xu fullname: Xu, Hongzhang organization: School of Geodesy and Geomatics, Wuhan University, Wuhan, China – sequence: 8 givenname: Weiwei surname: Tan fullname: Tan, Weiwei organization: The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China – sequence: 9 givenname: Qianqian surname: Yang fullname: Yang, Qianqian organization: School of Geodesy and Geomatics, Wuhan University, Wuhan, China – sequence: 10 givenname: Jiwen surname: Wang fullname: Wang, Jiwen organization: School of Geodesy and Geomatics, Wuhan University, Wuhan, China – sequence: 11 givenname: Jianhao surname: Gao fullname: Gao, Jianhao organization: School of Geodesy and Geomatics, Wuhan University, Wuhan, China – sequence: 12 givenname: Liangpei orcidid: 0000-0001-6890-3650 surname: Zhang fullname: Zhang, Liangpei organization: The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China |
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| Snippet | Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of... |
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| Title | Deep learning in environmental remote sensing: Achievements and challenges |
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