In this paper, the three-component power decomposition for polarimetric SAR (PolSAR) data with an adaptive volume scattering model is proposed. The volume scattering model is assumed to be reflection-symmetric but parameterized. For each image pixel, the decomposition first starts with determining the adaptive parameter based on matrix similarity metric. Then, a respective scattering power component is retrieved with the established procedure. It has been shown that the proposed method leads to complete elimination of negative powers as the result of the adaptive volume scattering model. Experiments with the PolSAR data from both the NASA/JPL (National Aeronautics and Space Administration/Jet Propulsion Laboratory) Airborne SAR (AIRSAR) and the JAXA (Japan Aerospace Exploration Agency) ALOS-PALSAR also demonstrate that the proposed method not only obtains similar/better results in vegetated areas as compared to the existing Freeman-Durden decomposition but helps to improve discrimination of the urban regions.

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 × 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.

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.