A Three-Hierarchy Evaluation of Polarimetric Performance of GF-3, Compared with ALOS-2/PALSAR-2 and RADARSAT-2

GaoFen-3 (GF-3) is the first Chinese civilian multi-polarization synthetic aperture radar (SAR) satellite, launched on 10 August of 2016, and put into operation at the end of January 2017. The polarimetric SAR (PolSAR) system of GF-3 is able to provide quad-polarization (quad-pol) images in a variety of geophysical research and applications. However, this ability increases the complexity of maintaining image quality and calibration. As a result, to evaluate the quality of polarimetric data, polarimetric signatures are necessary to guarantee accuracy. Compared with some other operational space-borne PolSAR systems, such as ALOS-2/PALSAR-2 (ALOS-2) and RADARSAT-2, GF-3 has less reported calibration and image quality files, forcing users to validate the quality of polarimetric imagery of GF-3 before quantitative applications. In this study, without the validation data obtained from a calibration infrastructure, an innovative, three-hierarchy strategy was proposed to assess PolSAR data quality, in which the performance of GF-3 data was evaluated with ALOS-2 and RADARSAT-2 data as references. Experimental results suggested that: (1) PolSAR data of GF-3 satisfied backscatter reciprocity, similar with that of RADARSAT-2; (2) most of the GF-3 PolSAR images had no signs of polarimetric distortion affecting decomposition, and the system of GF-3 may have been improved around May 2017; and (3) the classification accuracy of GF-3 varied from 75.0% to 91.4% because of changing image-acquiring situations. In conclusion, the proposed three-hierarchy approach has the ability to evaluate polarimetric performance. It proved that the residual polarimetric distortion of calibrated GF-3 PolSAR data remained at an insignificant level, with reference to that of ALOS-2 and RADARSAT-2, and imposed no significant impact on the polarimetric decomposition components and classification accuracy.

[1]  Manabu Watanabe,et al.  Operational performance of the ALOS global systematic acquisition strategy and observation plans for ALOS-2 PALSAR-2 , 2014 .

[2]  Hiroyoshi Yamada,et al.  Four-component scattering model for polarimetric SAR image decomposition , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[3]  J. Huynen.  Phenomenological theory of radar targets , 1970 .

[4]  A. P. Luscombe,et al.  RADARSAT-2 calibration: proposed targets and techniques , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[5]  Philippe Rolland,et al.  10 Years of RADARSAT-2 Operations - Challenges and Improvements , 2018 .

[6]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[7]  Thomas Jagdhuber,et al.  Characterization of vegetation and soil scattering mechanisms across different biomes using P-band SAR polarimetry , 2016 .

[8]  Maycira Costa,et al.  Landcover classification of the Lower Nhecolândia subregion of the Brazilian Pantanal Wetlands using ALOS/PALSAR, RADARSAT-2 and ENVISAT/ASAR imagery , 2013 .

[9]  Carlos López-Martínez,et al.  Polarimetric SAR speckle noise model , 2003, IEEE Trans. Geosci. Remote. Sens..

[10]  Thomas L. Ainsworth,et al.  Assessment of System Polarization Quality for Polarimetric SAR Imagery and Target Decomposition , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Quan Chen,et al.  A Preliminary Evaluation of the GaoFen-3 SAR Radiation Characteristics in Land Surface and Compared With Radarsat-2 and Sentinel-1A , 2018, IEEE Geoscience and Remote Sensing Letters.

[12]  Masanobu Shimada,et al.  PALSAR Radiometric and Geometric Calibration , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Kenny James,et al.  RADARSAT-2 Mission Status: Capabilities Demonstrated and Image Quality Achieved , 2008 .

[14]  Martti Hallikainen,et al.  Multitemporal behavior of L- and C-band SAR observations of boreal forests , 1999, IEEE Trans. Geosci. Remote. Sens..

[15]  A. Luscombe,et al.  RADARSAT-2 SAR image quality and calibration operations , 2004 .

[16]  Bing Han,et al.  A Quality Assessment Method Based on Common Distributed Targets for GF-3 Polarimetric SAR Data , 2018, Sensors.

[17]  S. Fukuda,et al.  Support vector machine classification of land cover: application to polarimetric SAR data , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[18]  Lei Shi,et al.  Polarimetric Calibration and Quality Assessment of the GF-3 Satellite Images , 2018, Sensors.

[19]  Monique Bernier,et al.  Aspect and incidence angle sensitivity in ERS-1 SAR data , 1998 .

[20]  Jian Yang,et al.  Assessment of GF-3 Polarimetric SAR Data for Physical Scattering Mechanism Analysis and Terrain Classification , 2017, Sensors.

[21]  Zhang Qingjun,et al.  System Design and Key Technologies of the GF-3 Satellite , 2017 .

[22]  Luciano Vieira Dutra,et al.  A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region , 2012 .

[23]  R. Lucas,et al.  New global forest/non-forest maps from ALOS PALSAR data (2007–2010) , 2014 .

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

[25]  Claire Marais-Sicre,et al.  Sensitivity of TerraSAR-X, RADARSAT-2 and ALOS satellite radar data to crop variables , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[26]  Masanobu Shimada.  Global Earth Monitoring Using ALOS-2/Palsar-2: Initial Status of the ALOS-2 Calibration Phase , 2014 .

[27]  Anthony Luscombe.  Image quality and calibration of RADARSAT-2 , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[28]  Ridha Touzi,et al.  Polarimetric target scattering decomposition: A review , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[29]  L. Thirion-Lefevre,et al.  Cross-Polarization Amplitudes of Obliquely Orientated Buildings With Application to Urban Areas , 2017, IEEE Geoscience and Remote Sensing Letters.

[30]  S. Cloude.  Target decomposition theorems in radar scattering , 1985 .

[31]  J. Beck,et al.  An introduction to the RADARSAT-2 mission , 2004 .

[32]  T. Motohka,et al.  Advanced Land Observing Satellite-2 (ALOS-2) and its follow-on L-band SAR mission , 2017, 2017 IEEE Radar Conference (RadarConf).

[33]  A. P. Luscombe,et al.  Polarimetric calibration for RADARSAT-2 , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[34]  Laurent Ferro-Famil,et al.  Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier , 2001, IEEE Trans. Geosci. Remote. Sens..

[35]  Weidong Yu,et al.  The SAR Payload Design and Performance for the GF-3 Mission , 2017, Sensors.

[36]  Junli Chen,et al.  Study on the impact of Polarimetric calibration errors on terrain classification with PolInSAR , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[37]  Masanobu Shimada,et al.  Model-Based Polarimetric SAR Calibration Method Using Forest and Surface-Scattering Targets , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

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

[40]  Laetitia Thirion-Lefevre,et al.  Polarimetric Phase and Implications for Urban Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[41]  F. Sabins.  Remote Sensing: Principles and Interpretation , 1987 .