Details
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 9233-9236 |
| Number of pages | 4 |
| ISBN (electronic) | 9781538671504 |
| ISBN (print) | 9781538671511 |
| Publication status | Published - 31 Oct 2018 |
| Event | 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 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.
Keywords
- ALOS-2, Object-based classification, RADARSAT-2, Random forest, TerraSAR-X, Wetland
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Earth and Planetary Sciences(all)
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2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 9233-9236 8517844.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A new hierarchical object-based classification algorithm for wetland mapping in Newfoundland, Canada
AU - Mohammadimanesh, Fariba
AU - Salehi, Bahram
AU - Mahdianpari, Masoud
AU - Motagh, Mahdi
N1 - Publisher Copyright: © 2018 IEEE Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - 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.
AB - 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.
KW - ALOS-2
KW - Object-based classification
KW - RADARSAT-2
KW - Random forest
KW - TerraSAR-X
KW - Wetland
UR - http://www.scopus.com/inward/record.url?scp=85063133655&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8517844
DO - 10.1109/IGARSS.2018.8517844
M3 - Conference contribution
AN - SCOPUS:85063133655
SN - 9781538671511
SP - 9233
EP - 9236
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -