Two Component Decomposition of Dual Polarimetric HH/VV SAR Data: Case Study for the Tundra Environment of the Mackenzie Delta Region, Canada

Abstract

This study investigates a two component decomposition technique for HH/VV-polarized PolSAR (Polarimetric Synthetic Aperture Radar) data. The approach is a straight forward adaption of the Yamaguchi decomposition and decomposes the data into two scattering contributions: surface and double bounce under the assumption of a negligible vegetation scattering component in Tundra environments. The dependencies between the features of this two and the classical three component Yamaguchi decomposition were investigated for Radarsat-2 (quad) and TerraSAR-X (HH/VV) data for the Mackenzie Delta Region, Canada. In situ data on land cover were used to derive the scattering characteristics and to analyze the correlation among the PolSAR features. The double bounce and surface scattering features of the two and three component scattering model (derived from pseudo-HH/VV-and quad-polarized data) showed similar scattering characteristics and positively correlated-R 2 values of 0.60 (double bounce) and 0.88 (surface scattering) were observed. The presence of volume scattering led to differences between the features and these were minimized for land cover classes of low vegetation height that showed little volume scattering contribution. In terms of separability, the quad-polarized Radarsat-2 data offered the best separation of the examined tundra land cover types and will be best suited for the classification. This is anticipated as it represents the largest feature space of all tested ones. However; the classes "wetland" and "bare ground" showed clear positions in the feature spaces of the C-and X-Band HH/VV-polarized data and an accurate classification of these land cover types is promising. Among the possible dual-polarization modes of Radarsat-2 the HH/VV was found to be the favorable mode for the characterization of the aforementioned tundra land cover classes due to the coherent acquisition and the preserved co-pol. phase. Contrary, HH/HV-polarized and VV/VH-polarized data were found to be best suited for the characterization of mixed and shrub dominated tundra.

A Novel Two-Component Decomposition for Co-Polar Channels of Gf-3 Quad-Pol Data

“…But most methods of target decomposition are based on fully polarized data (quad pol) and seldom utilize dual-polar data for target decomposition. Studies that highlight the benefit and applicability of HH/VV-polarized data are very small (Ullmann et al, 2016). It may be a pity for a number of dual-channels SAR data to make use of target decomposition theory.…”

Detection of Bivalve Beds on Exposed Intertidal Flats Using Polarimetric SAR Indicators

“…Common polarimetric decompositions are either coherent or incoherent. The coherent decompositions based on the Sinclair matrix prohibit multi-looking and require descriptions of distributed targets, while the incoherent decompositions based on either the covariance or the coherency matrix often require large look numbers to keep radiometric stability [23][24][25]. Therefore, the Kennaugh formulation appears to be the right balance due to the basic description of the backscattering process [24].…”

“…The limited time-window requires larger areal coverage with fine resolution. Therefore, further research is needed on the capabilities of existing or newly acquired dual-copolarization (dual-copol; HH + VV) SAR data [23] for monitoring intertidal flats, especially for the detection and classification of sediments and bivalve beds.…”

Scattering Characteristics of X-, C- and L-Band PolSAR Data Examined for the Tundra Environment of the Tuktoyaktuk Peninsula, Canada

“…Select studies that employed Synthetic Aperture Radar (SAR), polarimetric SAR (PolSAR) and InSAR (SAR interferometry) data and methods for the characterization of tundra (permafrost) landscapes and their dynamics; ERS: European Remote Sensing Satellite; R-1: Radarsat-1; TSX: TerraSAR-X; R-2: Radarsat-2; ALOS: Advanced Land Observing Satellite. [16,17] Mackenzie Delta Region, Canada R-2 Classification of tundra land cover and shoreline types via PolSAR 2011 & 2014 [6,7] Lena Delta, Russia TSX R-2 Characterization of melt onset and geomorphological units via PolSAR 2012 [8] Alaska, USA TSX Characterization of post-drainage succession via time series analysis and PolSAR 2012 [9] Sodankylä, Finland R-2 Identification of soil freezing and thawing states 2014 [10] Richards Island, Canada TSX R-2 Classification of tundra land cover via PolSAR 2014 [11] Most of the studies identified in Table 1, employed data of a single sensor and only few involved multi-frequency SAR/PolSAR/InSAR information, e.g., dealing with some combination of X-, C-or L-Band data [8,11,14,17]. Among the selected studies, the quad-polarization mode of Radarsat-2 was most frequently employed, followed by the dual HH/HV-polarization mode of ALOS and TerraSAR-X, and the dual HH/VV-polarization mode of TerraSAR-X.…”

“…For these different datasets, several polarimetric decomposition approaches were applied, including: the Eigen-decomposition (with the features Entropy, Anisotropy and Alpha Scattering Angle) [25,26], the Yamaguchi Decomposition [27], the Freeman-Durden Decomposition [28], and the Touzi Decomposition [29]. Two decomposition models for HH/VV-polarized data were further proposed by [30] and recently by [14].…”