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Hybrid Dual-Polarization Synthetic Aperture Radar
Open AccessArticle

Mid-season Crop Classification Using Dual-, Compact-, and Full-Polarization in Preparation for the Radarsat Constellation Mission (RCM)

1
C-CORE and Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
2
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
3
CRC-Laboratory in Advanced Geomatics Image Processing, Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB NB E3B 5A3, Canada
4
Environmental Resources Engineering, College of Environmental Science and Forestry, State University of New York, NY 13210, USA
5
Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec, QC G1K 9A9, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1582; https://doi.org/10.3390/rs11131582
Received: 19 May 2019 / Revised: 25 June 2019 / Accepted: 26 June 2019 / Published: 3 July 2019
(This article belongs to the Special Issue Compact Polarimetric SAR)
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Abstract

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm. View Full-Text
Keywords: crop classification; RADARSAT-2; compact-polarimetry; RADARSAT Constellation Mission; RCM; multi-temporal; multi-polarization crop classification; RADARSAT-2; compact-polarimetry; RADARSAT Constellation Mission; RCM; multi-temporal; multi-polarization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Mahdianpari, M.; Mohammadimanesh, F.; McNairn, H.; Davidson, A.; Rezaee, M.; Salehi, B.; Homayouni, S. Mid-season Crop Classification Using Dual-, Compact-, and Full-Polarization in Preparation for the Radarsat Constellation Mission (RCM). Remote Sens. 2019, 11, 1582.

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