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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 14; s. 2001 - 2016
Hlavní autoři:	Peng, Bo, Huang, Qunying, Vongkusolkit, Jamp, Gao, Song, Wright, Daniel B., Fang, Zheng N., Qiang, Yi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!