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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Vydáno v:	Remote sensing of environment Ročník 241; s. 111716
Hlavní autoři:	Yuan, Qiangqiang, Shen, Huanfeng, Li, Tongwen, Li, Zhiwei, Li, Shuwen, Jiang, Yun, Xu, Hongzhang, Tan, Weiwei, Yang, Qianqian, Wang, Jiwen, Gao, Jianhao, Zhang, Liangpei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Elsevier Inc 01.05.2020 Elsevier BV
Témata:	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
On-line přístup:	Získat plný text
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