Utilization of four-component scattering power decomposition method for glaciated terrain classification

Glaciated terrain classification is important for hydrological and climate change modelling. For this purpose, fully polarimetric Advanced Land Observation Satellite-Phase Array L-band Synthetic Aperture Radar (ALOS-PALSAR) data has been used over Indian Himalayan glaciated region. PALSAR data has been analyzed based on the three and four component scattering decomposition methods for glaciated terrain classification. These methods have been applied on multi-looked 3 6 3 coherency matrix of ALOS-PALSAR data. The analysis of these methods shows that the Freeman and Durden three-component scattering power decomposition (3-CSPD) method has over estimation problem in volume backscattering component as compared to the Yamaguchi four-component scattering power decomposition (4-CSPD) method. After finding suitability of 4-CSPD method over Himalayan glaciated terrain, it has been combined with complex Wishart distribution for supervised classification of ALOS-PALSAR image. By this way, an overall accuracy has been found to be 93.38%. Even this procedure shows very high accuracy but discrimination between vegetation and glacier snow/ice classes was not properly done. To overcome this ambiguity, the probability difference between surface backscattering and volume backscattering has been introduced as further steps in classification procedure.