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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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
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Shrnutí: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 realtime or fail to generalize well to other floods, for several reasons. First, multitemporal pixel-wise flood mapping requires accurate image registration, hindering the efficiency of large-scale processing. Although automatic image registration has been investigated, precisely coregistered multitemporal image sequence requires time-consuming fine tuning. Additionally, the floods may lead to the loss of many corresponding image points across multitemporal images for accurate coregistration. Second, existing unsupervised methods generally rely on hand-crafted features for floodwater detection. Such features may not well represent the patterns of floodwaters in different areas due to inconsistent weather conditions, illumination, and floodwater spectra. This article proposes a self-supervised learning framework for patch-wise urban flood mapping using bitemporal multispectral satellite imagery. Patch-wise change vector analysis is used with patch features learned through a self-supervised autoencoder to produce patch-wise change maps showing potentially flood-affected areas. Postprocessing including spectral and spatial filtering is applied to these patch-wise change maps to remove nonflood related changes. Final flood maps and parameter sensitivities were evaluated using several performance metrics. Two flood events from areas with differing degrees of urbanization were considered: Hurricane Harvey flood (2017) in Houston, Texas, and Hurricane Florence flood (2018) in Lumberton, North Carolina. The proposed method shows strong performance for self-supervised urban flood mapping.
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content type line 14
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2020.3047677