Evaluation of modified four-component scattering power decomposition method over highly rugged glaciated terrain

In recent years, there has been increased utilization of fully polarimetric synthetic aperture radar (POLSAR) data to study glaciated terrain features for glaciological and climate change modelling. This article is concerned with more accurate results and appropriate analysis of POLSAR data over a highly rugged glaciated area in Himalayan region. For this purpose, the modified Yamaguchi four-component scattering power decomposition (4-CSPD) method with a rotation concept of 3 × 3 coherency matrix [T] about line of sight is evaluated. It has been found that the modified Yamaguchi 4-CSPD method significantly improved the decomposition results as compared with the original 4-CSPD by minimizing the cross-polarized Horizontal-Vertical (HV) components. This modified 4-CSPD leads to enhancement in the double bounce scattering and surface scattering components and also avoids the overestimation problem in the volume scattering component as compared with the original 4-CSPD from the sloped terrain. The significant reductions of the negative power occurrence in the surface scattering (3.9%) and the double bounce scattering (19.7%) components have also been noticed as compared with the original 4-CSPD method over the glaciated area in this part of the Indian Himalaya.

[1]  J. Dozier.  Spectral Signature of Alpine Snow Cover from the Landsat Thematic Mapper , 1989 .

[2]  Yoshio Yamaguchi,et al.  Experimental study of microwave transmission in snowpack , 1990 .

[3]  Helmut Rott,et al.  Thematic studies in alpine areas by means of polarimetric SAR and optical imagery , 1994 .

[4]  Jiancheng Shi,et al.  Inferring snow wetness using C-band data from SIR-C's polarimetric synthetic aperture radar , 1995, IEEE Trans. Geosci. Remote. Sens..

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

[6]  K. C. Partington.  Discrimination of glacier facies using multi-temporal SAR data , 1998 .

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

[8]  Jiancheng Shi.  Estimation of snow water equivalence using SIR-C/X-SAR , 1998, Asia-Pacific Environmental Remote Sensing.

[9]  P. Vachon,et al.  InSAR results from the RADARSAT Antarctic Mapping Mission data: estimation of glacier motion using a simple registration procedure , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[10]  R. Michel,et al.  Flow of Glaciar Moreno, Argentina, from repeat-pass Shuttle Imaging Radar images: comparison of the phase correlation method with radar interferometry , 1999, Journal of Glaciology.

[11]  Thomas L. Ainsworth,et al.  Polarimetric SAR data compensation for terrain azimuth slope variation , 2000, IEEE Trans. Geosci. Remote. Sens..

[12]  Jiancheng Shi,et al.  Estimation of snow water equivalence using SIR-C/X-SAR. I. Inferring snow density and subsurface properties , 2000, IEEE Trans. Geosci. Remote. Sens..

[13]  Thomas L. Ainsworth,et al.  On the estimation of radar polarization orientation shifts induced by terrain slopes , 2002, IEEE Trans. Geosci. Remote. Sens..

[14]  Ya-Qiu Jin,et al.  Deorientation theory of polarimetric scattering targets and application to terrain surface classification , 2005, IEEE Trans. Geosci. Remote. Sens..

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

[16]  Gulab Singh,et al.  Feasibility of various remote sensing data for mapping snow cover area around Gangotri Glacier , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

[17]  Hiroyoshi Yamada,et al.  POLSAR Image Analysis of Wetlands Using a Modified Four-Component Scattering Power Decomposition , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[18]  G. Venkataraman,et al.  LOS PALSAR data analysis of snow cover area in Himalayan region using four component scattering decomposition technique , 2008, 2008 International Conference on Recent Advances in Microwave Theory and Applications.

[19]  Vikramaditya Dave.  Electrical and Electronics Engineering , 2009 .

[20]  E. Pottier,et al.  Polarimetric Radar Imaging: From Basics to Applications , 2009 .

[21]  Yoshizawa Shin,et al.  3D reconstruction of HIFU(high intensity focused ultrasound) pressure field from optical mearuement. , 2010 .

[22]  Y. Yamaguchi,et al.  Glaciated terrain classification using modified four component scattering decomposition model(Remote Sensing,ICSANE 2010 (International Conference on Space, Aeronautical and Navigational Electronics)) , 2010 .

[23]  Yamaguchi Yoshio,et al.  Glaciated terrain classification using modified four component scattering decomposition model , 2010 .

[24]  Hiroyoshi Yamada,et al.  Four-Component Scattering Power Decomposition With Rotation of Coherency Matrix , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Yoshio Yamaguchi,et al.  Utilization of four-component scattering power decomposition method for glaciated terrain classification , 2011 .

[26]  Yoshio Yamaguchi,et al.  4-Component Scattering Power Decomposition with phase rotation of coherency matrix , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[27]  Thomas L. Ainsworth,et al.  The Effect of Orientation Angle Compensation on Coherency Matrix and Polarimetric Target Decompositions , 2010, IEEE Transactions on Geoscience and Remote Sensing.