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Flood Classification in a Natural Wetland for Early Spring Conditions Using Various Polarimetric SAR Methods
Abstract
Abstract--- One of the major limitations of remote sensing flood detection is the presence of vegetation. Our study focuses on a flood classification using Radarsat-2 Quad-Pol data in a natural floodplain during leafless, dry vegetation (early spring) state. We conducted a supervised classification of a data set composed of nine polarimetric decompositions and Shannon entropy followed by the predictors' importance estimation to reveal which decomposed component had the strongest effect on classification models. Also, we tested two variants of polarimetric speckle filtering to see if this step influences the results. The classification accuracy was 0.78 and 0.83 for the boxcar and improved Lee sigma filter respectively. The Cloude - Pottier decomposition produced the highest accuracy (0.67) in a single-decomposition scenario, but the volume component of Pauli decomposition was the most important for classification in a multi-decomposition scenario.
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/IGARSS47720.2021.9554713
- License
- Copyright (2021 IEEE)
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS strony 6853 - 6856
- Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Berezowski T., Gierszewska M., Bieliński T.: Flood Classification in a Natural Wetland for Early Spring Conditions Using Various Polarimetric SAR Methods// 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS/ : , 2021, s.6853-6856
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/igarss47720.2021.9554713
- Sources of funding:
- Verified by:
- Gdańsk University of Technology
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