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

• Published in: Remote Sensing
• Main Authors: Duguay, Yannick, Bernier, Monique, Lévesque, Esther, Dominé, Florent
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2016
• Subjects: SAR; polarimetry; sub-Arctic; classification; support vector machine; Arctic; Subarctic
• Online Access: https://espace.inrs.ca/id/eprint/4688/; https://espace.inrs.ca/id/eprint/4688/1/P2966.pdf; https://doi.org/10.3390/rs8090697

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.