@article{oai:niigata-u.repo.nii.ac.jp:00001724,
 author = {Singh, Gulab and Venkataraman, Gopalan and 山口, 芳雄 and Park, Sang-Eun},
 issue = {2},
 journal = {IEEE transactions on geoscience and remote sensing, IEEE transactions on geoscience and remote sensing},
 month = {Feb},
 note = {This study examines the capability assessment of fully polarimetric L-band data for snow and non-snow area classification. The data sets used are the fully polarimetric Advanced Land Observation Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) data, optical (ALOS-AVNIR) data close to the radar acquisition, and ENVISAT-ASAR data. Several parameters are used to discriminate snow from non-snow-covered areas in the Indian Himalaya region, including backscattering coefficients, the ratio of cross/co-polarized backscattering power and polarization fraction value. Supervised classification schemes are employed using polarimetric decomposition methods based on the complex Wishart classifier. The accuracy of the classification was found to be 97.95% for the Wishart supervised classification. Among various parameters and methods, it was found that the alternative newly proposed Polarization Fraction (PF) scheme, based on the implementation of fully polarimetric synthetic aperture radar (POL-SAR) data, yielded the best classification result in the absence of training samples. The PF value has been effective for discrimination of snow-covered from non-snow-covered areas, debris covered glacier, and vegetation. The results of this investigation show that L-band fully polarimetric SAR data provide considerable improvement but may not possess the optimal capability to discriminate snow from other inherent natural and man-made scatterers in heavy snow laden mountainous scenarios which may require fully polarimetric S-Band or C-Band POLSAR measurements.},
 pages = {1177--1196},
 title = {Capability Assessment of Fully Polarimetric ALOS-PALSAR data for Discriminating Wet Snow from Other Scattering Types in Mountainous Regions},
 volume = {52},
 year = {2014}
}