Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery
Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to...
Main Authors: | Bo Peng, Zonglin Meng, Qunying Huang, Caixia Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-10-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/11/21/2492 |
Similar Items
-
Urban Flood Mapping With Bitemporal Multispectral Imagery Via a Self-Supervised Learning Framework
by: Bo Peng, et al.
Published: (2021-01-01) -
Detection of Surface Water and Floods with Multispectral Satellites
by: Cinzia Albertini, et al.
Published: (2022-11-01) -
Similar Patch Selection in Embedding Space for Multi-View Image Denoising
by: Geunwoo Oh, et al.
Published: (2021-01-01) -
patchIT: A Multipurpose Patch Creation Tool for Image Processing Applications
by: Anastasios L. Kesidis, et al.
Published: (2022-12-01) -
Bayesian calibration of a flood simulator using binary flood extent observations
by: Balbi, Mariano, et al.
Published: (2024)