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

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.

[1]  Irena Hajnsek,et al.  Polarimetric Soil Moisture Retrieval at Short Wavelength , 2013 .

[2]  Eric Pottier,et al.  Introduction to the Polarimetric Target Decomposition Concept , 2017 .

[3]  Hiroyoshi Yamada,et al.  A four-component decomposition of POLSAR images based on the coherency matrix , 2006, IEEE Geoscience and Remote Sensing Letters.

[4]  Thomas Jagdhuber,et al.  Classification and Monitoring of Reed Belts Using Dual-Polarimetric TerraSAR-X Time Series , 2016, Remote. Sens..

[5]  Philip H. Swain,et al.  A Result from Studies of Transformed Divergence , 1973 .

[6]  Andreas Schmitt,et al.  Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery - Richards Island, Canada , 2014, Remote. Sens..

[7]  Zhang Shouxua,et al.  Thematic Information Extraction of High Resolution Imagery Based on Object-oriented Classification , 2013 .

[8]  Guido Grosse,et al.  Characterizing Post-Drainage Succession in Thermokarst Lake Basins on the Seward Peninsula, Alaska with TerraSAR-X Backscatter and Landsat-based NDVI Data , 2012, Remote. Sens..

[9]  Irena Hajnsek,et al.  Dual-Polarimetry for Soil Moisture Inversion at X-Band , 2014 .

[10]  Pascale Dubois-Fernandez,et al.  COmpact Polarimetry Potentials , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Laurence C. Smith,et al.  Delineation of delta ecozones using interferometric SAR phase coherence: Mackenzie River Delta, N.W.T., Canada , 2001 .

[12]  Steven V. Kokelj,et al.  The environment and permafrost of the Mackenzie Delta area , 2009 .

[13]  Jean-Claude Souyris,et al.  Compact polarimetry based on symmetry properties of geophysical media: the /spl pi//4 mode , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Stefan Dech,et al.  Classification of Arctic Coastal land covers with polarimetric SAR data , 2013, 2013 IEEE Radar Conference (RadarCon13).

[15]  François Charbonneau,et al.  Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data , 2014, Remote. Sens..

[16]  Irena Hajnsek,et al.  Soil Moisture Inversion Using Hybrid Polarimetric RISAT-1 Data , 2014 .

[17]  Optimum Band Selection for Supervised Classification of Multispectral Data , 2007 .

[18]  Thomas Jagdhuber,et al.  Soil parameter retrieval under vegetation cover using SAR polarimetry , 2012 .

[19]  Eric Pottier,et al.  A review of target decomposition theorems in radar polarimetry , 1996, IEEE Trans. Geosci. Remote. Sens..

[20]  S. Cloude Polarisation: Applications in Remote Sensing , 2009 .

[21]  Fernando Vicente-Guijalba,et al.  Polarimetric Response of Rice Fields at C-Band: Analysis and Phenology Retrieval , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Irena Hajnsek,et al.  Soil Moisture Estimation Using Dual-Polarimetric Coherent (HH/VV) TerraSAR-X and TanDEM-X Data , 2013 .

[23]  Stefan Hinz,et al.  The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation , 2015 .

[24]  W. Xiong Communications Comments on "Compact Polarimetry Based on Symmetry Properties of Geophysical Media: The π/4 Mode" , 2006 .

[25]  Shane R. Cloude,et al.  DUAL VERSUS QUADPOL: A NEW TEST STATISTIC FOR RADAR POLARIMETRY , 2009 .

[26]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[27]  Jakob van Zyl,et al.  Advanced Polarimetric Concepts , 2011 .

[28]  Ranga B. Myneni,et al.  Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems , 2004 .

[29]  Ian G. W. Corns Arctic plant communities east of the Mackenzie Delta , 1974 .

[30]  Stefan Dech,et al.  CLASSIFICATION OF COASTAL ARCTIC LAND COVER BY MEANS OF TERRASAR-X DUAL CO-POLARIZED DATA , 2013 .

[31]  Amine Merzouki,et al.  Assessing RADARSAT-2 for Mapping Shoreline Cleanup and Assessment Technique (SCAT) Classes in the Canadian Arctic , 2014 .

[32]  Brian Brisco,et al.  A comparison of TerraSAR-X, RADARSAT-2 and ALOS-PALSAR interferometry for monitoring permafrost environments, case study from Herschel Island, Canada , 2011 .

[33]  J. S. Lee,et al.  A review of polarimetry in the context of synthetic aperture radar: concepts and information extraction , 2004 .

[34]  Andreas Schmitt,et al.  Wetland Monitoring Using the Curvelet-Based Change Detection Method on Polarimetric SAR Imagery , 2013 .

[35]  Hao Chen,et al.  Compact decomposition theory for L-Band satellite radar applications , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[36]  Ralf Ludwig,et al.  USING TERRASAR-X IMAGERY FOR THE MONITORING OF PERMAFROST DYNAMICS IN NORTHERN QUEBEC , 2011 .

[37]  Irena Hajnsek,et al.  Towards a detection of grassland cutting practices with dual polarimetric TerraSAR-X data , 2013 .

[38]  Chung-Lin Huang,et al.  Image analysis and interpretation for semantics categorization in baseball video , 2003, Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing.

[39]  Mitsunobu Sugimoto,et al.  On the eigenvalue analysis using HH-VV dual-polarization SAR data and its applications to monitoring of coastal oceans , 2013, Defense, Security, and Sensing.

[40]  Irena Hajnsek,et al.  Identification of Soil Freezing and Thawing States Using SAR Polarimetry at C-Band , 2014, Remote. Sens..

[41]  François Charbonneau,et al.  SAR Interferometry and Polarimetry for Mapping and Monitoring Permafrost in Canada , 2009 .