Characterizing Scattering Behaviour and Assessing Potential for Classification of Arctic Shore and Near-Shore Land Covers with Fine Quad-Pol RADARSAT-2 Data

Abstract

Freeman-Durden and Cloude-Pottier decompositions were applied to polarimetric RADARSAT-2 data to characterize the scattering behaviour of shore and near-shore land cover types for two study areas in the Beaufort Sea, Canada: Richards Island and Tuktoyaktuk Harbour. The impact of incidence angle was evaluated through comparison of Single Look Complex Fine Quad-Pol images acquired at three angles: shallow (45.3 • -49.5 • ), medium (39.3 • -41.6 • ), and steep (20.9 • -24.2 • ), and the potential for accurate classification was assessed using three unsupervised polarimetric SAR classifiers: Wishart-entropy/alpha, Wishart-entropy/anisotropy/alpha, and Freeman-Wishart. Results indicate that overall, medium, and shallow incidence angle images provide more useful information than steep angle images and that these classifiers are capable of distinguishing some general land cover types, including substrates, wetlands, vegetated tundra, and water. Results from this analysis will inform future research related to classification and interpretation of RADARSAT-2 data for shoreline mapping. Résumé. Des décompositions Freeman-Durden etCloude-Pottier ontété appliquéesà des données polarimétriques RADARSAT-2 pour caractériser le comportement de diffusion des différents types de couverture terrestre des milieux littoraux et côtiers pour deux zones d'étude dans la mer de Beaufort, au Canada: l'île Richards et le port de Tuktoyaktuk. L'impact de l'angle d'incidence aétéévalué par comparaison des images singulières complexes <<Single Look Complex (SLC)>> quad-pol fines acquisesà trois angles; plus rasants (45.3 • -49.5 • ), moyens (39.3 • -41.6 • ) et abrupts (20.9 • -24.2 • ), tandis que le potentiel pour une classification précise aétéévaluéà l'aide de trois classificateurs RSO polarimétriques non dirigés: Wishart-entropie/alpha, Wishart-entropie/anisotropie/alpha et Freeman-Wishart.Les résultats indiquent que, dans l'ensemble, les images avec des angles d'incidence moyensà rasants fournissent des informations plus utiles que les images aux angles abrupts, et que ces classificateurs sont capables de distinguer certains types généraux de couverture terrestre, y compris: les substrats, les zones humides, la toundra végétalisée et l'eau. Les résultats de cette analyse seront utiles dans les recherches futures liéesà la classification età l'interprétation des données RADARSAT-2 pour la cartographie des rivages.

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

“…The authors demonstrated potential for classifier transferability by applying both models trained on data from the Richards Island site to the Ivvavik site, achieving overall accuracies of 71% and 78% for the pixel and object-based approaches, respectively. Notably, these results were attained with RADARSAT-2 images that were acquired on different dates, and at different incidence angles (~34°-36° (FQ15) over Richards Island, and ~48°-49° (FQ30) over Ivvavik), the latter of which has been shown to affect the backscattering behaviour of some shoreline classes [11,12].…”

“…In a follow-up paper Banks et al [12] assessed the potential to classify shore and near-shore land cover types using unsupervised polarimetric SAR classifiers, including: Wishart-entropy/alpha, Wishart-entropy/anisotropy/alpha, and Freeman-Wishart [18,19]. The authors applied each classifier to the same six images used by Banks et al [11], and found that they could detect more land covers using the shallow and medium incidence angle images.…”

“…Few studies have focused on assessing the potential for shoreline sensitivity mapping using Earth observation data, though of the studies that do exist, a number were undertaken in the Canadian Arctic [11][12][13]. Potential for this application has also been demonstrated in other regions [15,16].…”

“…In light of this, there is interest in developing a semi-automated mapping approach using Earth observation data [11][12][13]. Accordingly, the purpose of this study was to assess the potential to operationalize shoreline sensitivity mapping over a large region, using a single model to classify data that have been demonstrated to provide relevant and complementary information for this application [11][12][13]; specifically, multiple RADARSAT-2 Synthetic Aperture Radar (SAR), and Landsat 5 optical scenes (images necessarily acquired on different dates and with different spatial footprints to provide full study site coverage), as well as a Digital Elevation Model (DEM). For this we used the Random Forest algorithm; a non-parametric classifier based on an ensemble of individual decision tree models [14].…”

Random Forest Classification of Sediments on Exposed Intertidal Flats Using Alos-2 Quad-Polarimetric Sar Data

“…Former studies have thrown light on the potentials of the features extracted from optical and SAR data source and abilities of discrimination of tidal water, sediment and other biotic community. (Baghdadi et al, 2013;Banks et al, 2014a;Banks et al, 2014b;Brockmann and Stelzer, 2008;Gade et al, 2008;Koch et al, 2012;Ullmann et al, 2014). But there are rarely studies focus on subdividing sediment types which can promote economic development and environmental protection.…”