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Remote Sens. 2016, 8(9), 697;

Land Cover Classification in SubArctic Regions Using Fully Polarimetric RADARSAT-2 Data

Centre Eau Terre Environnement, Institut national de la recherche scientifique (INRS), 490 de la Couronne, Québec City , QC G1K 9A9, Canada
Centre d’études nordiques, Laval University, Pavillon Abitibi-Price 2405, rue de la Terrasse Local 1202, Québec City, QC G1V 0A6, Canada
Université du Québec à Trois-Rivières, 3351, boul. des Forges, Trois-Rivières, QC G9A 5H7, Canada
Takuvik Joint International Laboratory, Laval University (Canada) and CNRS (France), Pavillon Alexandre Vachon, 1045 avenue de la Médecine, Québec City , QC G1V 0A6, Canada
Author to whom correspondence should be addressed.
Academic Editors: Zhong Lu, Richard Gloaguen and Prasad S. Thenkabail
Received: 26 May 2016 / Revised: 25 July 2016 / Accepted: 9 August 2016 / Published: 24 August 2016
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The expansion of shrub vegetation in Arctic and sub-Arctic environments observed in the past decades can have significant effects on northern ecosystems. There is a need for efficient tools to monitor those changes, not only in terms of the spatial coverage of shrubs, but also their vertical growth. The objective of the current paper is to evaluate the performance of polarimetric C-band SAR datasets for land cover classification in sub-Arctic environments. A series of RADARSAT-2 quad-pol images were acquired between October 2011 and April 2012. The Support Vector Machine (SVM) classification scheme was used on three sets of features: the elements of the polarimetric coherency matrix [ T ] , the parameters extracted from a polarimetric decomposition based on the eigenvalues and eigenvectors of [ T ] and the parameters extracted from a model-based decomposition. Using a single image, the results show that the best classification accuracies ( 75 % ) are obtained using the [ T ] matrix with the October images. When adding a second image to the feature set, either from two different dates or two incidence angles, the classification accuracy is improved and reaches 90 . 1 % with two images from October 2011 and April 2012 at 27 incidence. The results show that C-band polarimetric SAR imagery is an adequate tool to map shrub vegetation in sub-Arctic environments. View Full-Text
Keywords: SAR; polarimetry; sub-Arctic; classification; support vector machine SAR; polarimetry; sub-Arctic; classification; support vector machine

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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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Duguay, Y.; Bernier, M.; Lévesque, E.; Domine, F. Land Cover Classification in SubArctic Regions Using Fully Polarimetric RADARSAT-2 Data. Remote Sens. 2016, 8, 697.

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