A new hierarchical object-based classification algorithm for wetland mapping in Newfoundland, Canada

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

Autoren

  • Fariba Mohammadimanesh
  • Bahram Salehi
  • Masoud Mahdianpari
  • Mahdi Motagh

Organisationseinheiten

Externe Organisationen

  • Memorial University of Newfoundland
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des Sammelwerks2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten9233-9236
Seitenumfang4
ISBN (elektronisch)9781538671504
ISBN (Print)9781538671511
PublikationsstatusVeröffentlicht - 31 Okt. 2018
Veranstaltung38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spanien
Dauer: 22 Juli 2018 → 27 Juli 2018

Abstract

In this study, a new hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a study area located in the north eastern portion of the Avalon Peninsula, Newfoundland and Labrador province, Canada. Specifically, multi-polarization and multi-frequency SAR data, including single polarized TerraSAR-X (HH), dual polarized L-band ALOS-2 (HH/HV), and fully polarized C-band RADARSAT-2 images, were applied in three different classification levels. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. Importantly, an overall accuracy of 94.82% was obtained for the final classified map in this study.

ASJC Scopus Sachgebiete

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A new hierarchical object-based classification algorithm for wetland mapping in Newfoundland, Canada. / Mohammadimanesh, Fariba; Salehi, Bahram; Mahdianpari, Masoud et al.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. S. 9233-9236 8517844.

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

Mohammadimanesh, F, Salehi, B, Mahdianpari, M & Motagh, M 2018, A new hierarchical object-based classification algorithm for wetland mapping in Newfoundland, Canada. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8517844, Institute of Electrical and Electronics Engineers Inc., S. 9233-9236, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spanien, 22 Juli 2018. https://doi.org/10.1109/IGARSS.2018.8517844
Mohammadimanesh, F., Salehi, B., Mahdianpari, M., & Motagh, M. (2018). A new hierarchical object-based classification algorithm for wetland mapping in Newfoundland, Canada. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (S. 9233-9236). Artikel 8517844 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8517844
Mohammadimanesh F, Salehi B, Mahdianpari M, Motagh M. A new hierarchical object-based classification algorithm for wetland mapping in Newfoundland, Canada. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. S. 9233-9236. 8517844 doi: 10.1109/IGARSS.2018.8517844
Mohammadimanesh, Fariba ; Salehi, Bahram ; Mahdianpari, Masoud et al. / A new hierarchical object-based classification algorithm for wetland mapping in Newfoundland, Canada. 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. S. 9233-9236
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title = "A new hierarchical object-based classification algorithm for wetland mapping in Newfoundland, Canada",
abstract = "In this study, a new hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a study area located in the north eastern portion of the Avalon Peninsula, Newfoundland and Labrador province, Canada. Specifically, multi-polarization and multi-frequency SAR data, including single polarized TerraSAR-X (HH), dual polarized L-band ALOS-2 (HH/HV), and fully polarized C-band RADARSAT-2 images, were applied in three different classification levels. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. Importantly, an overall accuracy of 94.82% was obtained for the final classified map in this study.",
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note = "Publisher Copyright: {\textcopyright} 2018 IEEE Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",
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AU - Mohammadimanesh, Fariba

AU - Salehi, Bahram

AU - Mahdianpari, Masoud

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