elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Urban flood mapping with an active self-learning convolutional neural network based on TerraSAR-X intensity and interferometric coherence

Li, Yu and Martinis, Sandro and Wieland, Marc (2019) Urban flood mapping with an active self-learning convolutional neural network based on TerraSAR-X intensity and interferometric coherence. ISPRS Journal of Photogrammetry and Remote Sensing, 152, pp. 178-191. Elsevier. DOI: 10.1016/j.isprsjprs.2019.04.014 ISSN 0924-2716

Full text not available from this repository.

Official URL: https://www.sciencedirect.com/science/article/abs/pii/S092427161930111X

Abstract

Synthetic Aperture Radar (SAR) remote sensing has been widely used for flood mapping and monitoring. Nevertheless, flood detection in urban areas still proves to be particularly challenging by using SAR. In this paper, we assess the roles of SAR intensity and interferometric coherence in urban flood detection using multi-temporal TerraSAR-X data. We further introduce an active self-learning convolution neural network (A-SL CNN) framework to alleviate the effect of a limited annotated training dataset. The proposed framework selects informative unlabeled samples based on a temporal-ensembling CNN model. These samples are subsequently pseudo-labeled by a multi-scale spatial filter. Consistency regularization is introduced to penalize incorrect labels caused by pseudo-labeling. We show results for a case study that is centered on flooded areas in Houston, USA, during hurricane Harvey in August 2017. Our experiments show that multi-temporal intensity (pre- and co-event) plays the most important role in urban flood detection. Adding multi-temporal coherence can increase the reliability of the inundation map considerably. Meanwhile, encouraging results are achieved by the proposed A-SL CNN framework: the k statistic is improved from 0.614 to 0.686 in comparison to its supervised counterpart.

Item URL in elib:https://elib.dlr.de/127744/
Document Type:Article
Title:Urban flood mapping with an active self-learning convolutional neural network based on TerraSAR-X intensity and interferometric coherence
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Li, Yuyu.li (at) dlr.deUNSPECIFIED
Martinis, Sandrosandro.martinis (at) dlr.dehttps://orcid.org/0000-0002-6400-361X
Wieland, Marcmarc.wieland (at) dlr.dehttps://orcid.org/0000-0002-1155-723X
Date:2019
Journal or Publication Title:ISPRS Journal of Photogrammetry and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:152
DOI :10.1016/j.isprsjprs.2019.04.014
Page Range:pp. 178-191
Publisher:Elsevier
ISSN:0924-2716
Status:Published
Keywords:Urban flooding; Multi-temporal SAR; Interferometric coherence; Active learning; Self-learning; Convolution neural network
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Martinis, Sandro
Deposited On:19 Jun 2019 09:34
Last Modified:06 Sep 2019 15:28

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.