Urban Flood Mapping With Bitemporal Multispectral Imagery Via a Self-Supervised Learning Framework

Near realtime flood mapping in densely populated urban areas is critical for emergency response. The strong heterogeneity of urban areas poses a big challenge for accurate near realtime flood mapping. However, previous studies on automatic methods for urban flood mapping perform infeasible in near r...

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Vydané v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 14; s. 2001 - 2016
Hlavní autori:	Peng, Bo, Huang, Qunying, Vongkusolkit, Jamp, Gao, Song, Wright, Daniel B., Fang, Zheng N., Qiang, Yi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Emergency preparedness Emergency response Flood control Flood mapping Floods Floodwater Handicrafts Heterogeneity Hurricanes Image registration Imagery Labeling Mapping Methods multispectral (MS) imagery Optical imaging Optical sensors Parameter sensitivity Performance evaluation Performance measurement Population density Real time Satellite imagery Self-supervised learning Sensitivity analysis Spaceborne remote sensing Spatial filtering Spatial resolution Supervised learning urban Urban areas Urbanization Vector analysis Weather
ISSN:	1939-1404, 2151-1535
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