Application of incoherent target decomposition theorems to classify snow cover over the Himalayan region

Snow cover is an important parameter for hydrological modelling and climate change modelling. Various methods are available only for wet snow-cover mapping using conventional synthetic aperture radar (SAR) data. Total snow (wet + dry) cover mapping with SAR data is still a topical research area. Therefore, incoherent target decomposition theorems have been implemented on fully polarimetric SAR data to characterize the scattering of various targets. Further classification techniques – both unsupervised and supervised – have been applied for accurate mapping of total snow cover. For this purpose, Advanced Land Observing Satellite – phased array-type L-band SAR (ALOS–PALSAR) data (12 May 2007) have been analysed for snow classification of glaciated terrain in and around Badrinath region in Himalaya. An ALOS-Advanced Visible and Near Infrared Radiometer (AVNIR)-2 image (6 May 2007) was also used to provide assistance in the selection of different training classes. It has been found that the application of incoherent target decomposition theorems such as H/A/α and four-component scattering mechanism models are good for extracting the desired information of snow cover from fully polarimetric PALSAR data. Finally, based on these target decomposition theorems and the Wishart classifier, PALSAR data have been classified into snow or non-snow cover, and the user accuracy of snow classes was found to be better than the user accuracy of other classes. Hence, the application of incoherent target decomposition theorems with full polarimetric ALOS-PALSAR data is useful for snow-cover mapping.

[1]  Gulab Singh,et al.  ENVISAT-ASAR data analysis for snow cover mapping over Gangotri region , 2006, SPIE Asia-Pacific Remote Sensing.

[2]  Martti Hallikainen,et al.  The use of ERS-1 SAR data in snow melt monitoring , 1997, IEEE Trans. Geosci. Remote. Sens..

[3]  Audrey Martini,et al.  Télédétection d'un couvert neigeux en milieux alpins à partir de données SAR polarimétriques multi-fréquentielles et multi-temporelles , 2005 .

[4]  Ron Kwok,et al.  Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution , 1994 .

[5]  William L. Cameron,et al.  Simulated polarimetric signatures of primitive geometrical shapes , 1996, IEEE Trans. Geosci. Remote. Sens..

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

[7]  Martti Hallikainen,et al.  Accuracy assessment of SAR data-based snow-covered area estimation method , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Jiancheng Shi,et al.  Deriving snow liquid water content using C-band polarimetric SAR , 1993, Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium.

[9]  Rama Chellappa,et al.  Unsupervised segmentation of polarimetric SAR data using the covariance matrix , 1992, IEEE Trans. Geosci. Remote. Sens..

[10]  Ridha Touzi,et al.  Characterization of target symmetric scattering using polarimetric SARs , 2002, IEEE Trans. Geosci. Remote. Sens..

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

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

[13]  H. Zebker,et al.  Imaging radar polarization signatures: Theory and observation , 1987 .

[14]  J. J. van Zyl,et al.  Imaging radar polarimetry: a review , 1991, Proc. IEEE.

[15]  Jiancheng Shi,et al.  Measurements of snow- and glacier-covered areas with single-polarization SAR , 1993, Annals of Glaciology.

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

[17]  J. Zyl,et al.  Bayesian classification of polarimetric SAR images using adaptive a priori probabilities , 1992 .

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

[19]  Gulab Singh,et al.  The H/A/Alpha Polarimetric Decomposition Theorem and Complex Wishart Distribution for Snow Cover Monitoring , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[20]  Iain H. Woodhouse.  Polarimetric radar imaging: from basics to applications by Jong-Sen Lee and Eric Pottier , 2012 .

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

[22]  Leslie M. Novak,et al.  Optimal polarimetric processing for enhanced target detection , 1993 .

[23]  E. Krogager.  New decomposition of the radar target scattering matrix , 1990 .

[24]  F. Ulaby,et al.  Radar polarimetry for geoscience applications , 1990 .

[25]  Leslie M. Novak,et al.  Optimal Speckle Reduction In Polarimetric Sar Imagery* , 1988, Twenty-Second Asilomar Conference on Signals, Systems and Computers.

[26]  Zong-Guo Xia,et al.  A comprehensive evaluation of filters for radar speckle suppression , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.