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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Bibliographic Details
Published in:	Remote sensing of environment Vol. 241; p. 111716
Main Authors:	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
Language:	English
Published:	New York Elsevier Inc 01.05.2020 Elsevier BV
Subjects:	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
Online Access:	Get full text
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