Remote Sens. 2016, 8(12), 1027; https://doi.org/10.3390/rs8121027
Two Component Decomposition of Dual Polarimetric HH/VV SAR Data: Case Study for the Tundra Environment of the Mackenzie Delta Region, Canada
1
Institute Geography and Geology, University of Wuerzburg, D-97074 Wuerzburg, Germany
2
German Aerospace Center (DLR), German Remote Sensing Data Center, D-82234 Wessling, Germany
3
German Aerospace Center (DLR), Microwaves and Radar Institute, D-82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Zhong Lu and Prasad S. Thenkabail
Received: 11 July 2016 / Revised: 5 December 2016 / Accepted: 8 December 2016 / Published: 16 December 2016
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-R2 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. View Full-TextKeywords:
Synthetic Aperture Radar (SAR); Polarimetric Synthetic Aperture Radar (PolSAR); dual polarimetry; polarimetric decomposition; TerraSAR-X; Radarsat-2; tundra; arctic; Canada
▼
Figures
Figure 1
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).
Share & Cite This Article
MDPI and ACS Style
Ullmann, T.; Schmitt, A.; Jagdhuber, T. Two Component Decomposition of Dual Polarimetric HH/VV SAR Data: Case Study for the Tundra Environment of the Mackenzie Delta Region, Canada. Remote Sens. 2016, 8, 1027.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
Related Articles
Article Metrics
Comments
[Return to top]
Remote Sens.
EISSN 2072-4292
Published by MDPI AG, Basel, Switzerland
RSS
E-Mail Table of Contents Alert