Improving SAR-based flood detection in arid regions using texture features

DC FieldValueLanguage
dc.contributor.authorRitushree, Dk
dc.contributor.authorGarg, Shagun
dc.contributor.authorDasgupta, Antara
dc.contributor.authorMartinis, Sandro
dc.contributor.authorSelvakumaran, Sivasakthy
dc.contributor.authorMotagh, Mahdi
dc.date.accessioned2023-07-12T06:59:29Z-
dc.date.available2023-07-12T06:59:29Z-
dc.date.issued2023
dc.identifier.isbn9798350345421
dc.identifier.urihttp://osnascholar.ub.uni-osnabrueck.de/handle/unios/72096-
dc.descriptionCited by: 0; Conference name: 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023; Conference date: 27 January 2023 through 29 January 2023; Conference code: 187316
dc.description.abstractFlood monitoring in arid regions is challenging using Synthetic Aperture Radar (SAR) due to the similar backscatter of water and dry sand in surrounding areas. Since textural information is abundant in SAR images, this study investigates the added value of texture in SAR-based flood detection by providing it as auxiliary information for flood delineation. Results show that texture enhanced SAR images in VH polarization substantially underpredicts the flooded area, so adding texture does not improve the classification accuracy. However, using both polarization (VV and VH) produce ∼26% higher overall accuracy for flood detection in arid regions. © 2023 IEEE.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023
dc.subjectAdded values
dc.subjectArid regions
dc.subjectAuxiliary information
dc.subjectDry sand
dc.subjectFlood detections
dc.subjectFlood mapping
dc.subjectFlood monitoring
dc.subjectFloods
dc.subjectForestry
dc.subjectImage enhancement
dc.subjectImage texture
dc.subjectPolarization
dc.subjectRadar imaging
dc.subjectRandom Forest
dc.subjectRandom forests
dc.subjectSAR
dc.subjectSynthetic aperture radar
dc.subjectSynthetic aperture radar images
dc.subjectTextural information
dc.subjecttexture
dc.subjectTexture features
dc.subjectTextures
dc.titleImproving SAR-based flood detection in arid regions using texture features
dc.typeconference paper
dc.identifier.doi10.1109/MIGARS57353.2023.10064526
dc.identifier.scopus2-s2.0-85151277351
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85151277351&doi=10.1109%2fMIGARS57353.2023.10064526&partnerID=40&md5=0bb2836cb21b27fe07a11d1f4a23fba0
dcterms.isPartOf.abbreviationInt. Conf. Mach. Intell. GeoAnalytics Remote Sens., MIGARS
local.import.remainsaffiliations : RemoteSensing and Geoinformatics, (Geodesy), Gfz German Research Centre for Geosciences, Potsdam, Germany; University of Cambridge, Future Infrastructure and Built Environment (FIBE), Department of Engineering, Cambridge, United Kingdom; University of Osnabrück, Remote Sensing Working Group, Institute of Informatics, Osnabrück, Germany; German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhonefen, Germany; University of Cambridge, Department of Engineering, Cambridge, United Kingdom; Leibniz University Hannover, Institute for Photogrammetry and Geo-Information, Hannover, Germany
local.import.remainscorrespondence_address : D. Ritushree; RemoteSensing and Geoinformatics, (Geodesy), Gfz German Research Centre for Geosciences, Potsdam, Germany; email: dibakar@gfz-potsdam.de
local.import.remainspublication_stage : Final
Show simple item record

Page view(s)

10
Last Week
0
Last month
2
checked on Jul 16, 2024

Google ScholarTM

Check

Altmetric