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Added Value of Coherent Copolar Polarimetry at X-Band for Crop-Type Mapping

Busquierand, M. and Lopez-Sanchez, Juan M. and Bargiel, D. (2019):
Added Value of Coherent Copolar Polarimetry at X-Band for Crop-Type Mapping.
In: IEEE Geoscience and Remote Sensing Letters, IEEE, pp. 1-5, ISSN 1558-0571,
DOI: 10.1109/LGRS.2019.2933738,
[Online-Edition: https://ieeexplore.ieee.org/abstract/document/8809193],
[Article]

Abstract

A set of six spotlight TerraSAR-X images acquired at HH and VV polarizations in 2009 over an agricultural site in Germany are employed to evaluate the potential contribution of polarimetric features derived from this copolar mode to crop-type mapping. Results show that the inclusion of the correlation between copolar channels in the set of input features of the classifier consistently improves the classification performance with respect to the use of only backscattering coefficients. An increase around 8-10 in overall accuracy, depending on the experiment setup, is achieved. Both user and producer accuracies are improved for all crop types, being the most noticeable contribution for barley, oat, and sugar beet. Different sets of input features, as well as classification and evaluation strategies, are tested in order to assess the robustness of this contribution.

Item Type: Article
Erschienen: 2019
Creators: Busquierand, M. and Lopez-Sanchez, Juan M. and Bargiel, D.
Title: Added Value of Coherent Copolar Polarimetry at X-Band for Crop-Type Mapping
Language: English
Abstract:

A set of six spotlight TerraSAR-X images acquired at HH and VV polarizations in 2009 over an agricultural site in Germany are employed to evaluate the potential contribution of polarimetric features derived from this copolar mode to crop-type mapping. Results show that the inclusion of the correlation between copolar channels in the set of input features of the classifier consistently improves the classification performance with respect to the use of only backscattering coefficients. An increase around 8-10 in overall accuracy, depending on the experiment setup, is achieved. Both user and producer accuracies are improved for all crop types, being the most noticeable contribution for barley, oat, and sugar beet. Different sets of input features, as well as classification and evaluation strategies, are tested in order to assess the robustness of this contribution.

Journal or Publication Title: IEEE Geoscience and Remote Sensing Letters
Publisher: IEEE
Uncontrolled Keywords: Agriculture, Correlation, Training, Backscatter, Testing, Polarimetry, Synthetic aperture radar, Agriculture, classification, polarimetry, synthetic aperture radar (SAR)
Divisions: 13 Department of Civil and Environmental Engineering Sciences
13 Department of Civil and Environmental Engineering Sciences > Institute of Geodesy
13 Department of Civil and Environmental Engineering Sciences > Institute of Geodesy > Remote Sensing and Image Analysis
Date Deposited: 04 Oct 2019 06:28
DOI: 10.1109/LGRS.2019.2933738
Official URL: https://ieeexplore.ieee.org/abstract/document/8809193
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