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

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Autoren

  • Dk Ritushree
  • Shagun Garg
  • Antara Dasgupta
  • Sandro Martinis
  • Sivasakthy Selvakumaran
  • Mahdi Motagh

Organisationseinheiten

Externe Organisationen

  • Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum (GFZ)
  • Universität Osnabrück
  • Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
  • University of Cambridge
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des Sammelwerks2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)9798350345421
ISBN (Print)979-8-3503-4543-8
PublikationsstatusVeröffentlicht - 2023
Veranstaltung2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023 - Hyderabad, Indien
Dauer: 27 Jan. 2023 → 29 Jan. 2023

Abstract

Flood 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.

ASJC Scopus Sachgebiete

Zitieren

Improving SAR-based flood detection in arid regions using texture features. / Ritushree, Dk; Garg, Shagun; Dasgupta, Antara et al.
2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023. Institute of Electrical and Electronics Engineers Inc., 2023.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Ritushree, D, Garg, S, Dasgupta, A, Martinis, S, Selvakumaran, S & Motagh, M 2023, Improving SAR-based flood detection in arid regions using texture features. in 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023. Institute of Electrical and Electronics Engineers Inc., 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023, Hyderabad, Indien, 27 Jan. 2023. https://doi.org/10.1109/MIGARS57353.2023.10064526
Ritushree, D., Garg, S., Dasgupta, A., Martinis, S., Selvakumaran, S., & Motagh, M. (2023). Improving SAR-based flood detection in arid regions using texture features. In 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MIGARS57353.2023.10064526
Ritushree D, Garg S, Dasgupta A, Martinis S, Selvakumaran S, Motagh M. Improving SAR-based flood detection in arid regions using texture features. in 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023. Institute of Electrical and Electronics Engineers Inc. 2023 doi: 10.1109/MIGARS57353.2023.10064526
Ritushree, Dk ; Garg, Shagun ; Dasgupta, Antara et al. / Improving SAR-based flood detection in arid regions using texture features. 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023. Institute of Electrical and Electronics Engineers Inc., 2023.
Download
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title = "Improving SAR-based flood detection in arid regions using texture features",
abstract = "Flood 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.",
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author = "Dk Ritushree and Shagun Garg and Antara Dasgupta and Sandro Martinis and Sivasakthy Selvakumaran and Mahdi Motagh",
note = "Funding Information: ACKNOWLEDGMENT This work was supported by the Helmholtz project AI for Near-Real Time Satellite-based Flood Response (AI4Flood), which is a joint collaboration between the German Research Center for Geosciences (GFZ) and German Aerospace Center (DLR) and EPSRC Centre for Doctoral Training in Future ; 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023 ; Conference date: 27-01-2023 Through 29-01-2023",
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Download

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T1 - Improving SAR-based flood detection in arid regions using texture features

AU - Ritushree, Dk

AU - Garg, Shagun

AU - Dasgupta, Antara

AU - Martinis, Sandro

AU - Selvakumaran, Sivasakthy

AU - Motagh, Mahdi

N1 - Funding Information: ACKNOWLEDGMENT This work was supported by the Helmholtz project AI for Near-Real Time Satellite-based Flood Response (AI4Flood), which is a joint collaboration between the German Research Center for Geosciences (GFZ) and German Aerospace Center (DLR) and EPSRC Centre for Doctoral Training in Future

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AB - Flood 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.

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