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Hybrid Three-Component Scattering Power Characterization From Polarimetric SAR Data Isolating Dominant Scattering Mechanisms
Journal
IEEE Transactions on Geoscience and Remote Sensing
ISSN
01962892
Date Issued
2022-01-01
Author(s)
Maurya, Himanshu
Bhattacharya, Avik
Mishra, Amit Kumar
Panigrahi, Rajib Kumar
Abstract
Rapid advancements have been made in model-based decomposition techniques for polarimetric synthetic aperture radar (PolSAR) data. Improvements have been primarily driven by including additional scattering models to the three-component model-based method first introduced by Freeman and Durden. Nevertheless, the three-component method is still extensively used due to its simplicity and ease of interpretability. Recently, the paradigm of the decomposition strategy has been changed to non-model types with notable success. Thus, using this new approach, we propose a hybrid (i.e., combining non-model and model-based) three-component methodology in this work. The proposed method primarily involves three steps: 1) the generalized eigendecomposition technique is first used to determine the optimum volume scattering power; 2) the residual rank-2 coherency matrix (i.e., volume scattering model deducted) is appropriately transformed using two unitary transformations to decorrelate the odd- and even-bounce scattering components; and 3) compute the odd- and even-bounce scattering power contributions using the newly developed scattering-type parameter obtained from the rank-2 matrix. Each step carries relevant physical significance that is appropriately addressed in this work. The proposed methodology is first demonstrated using some specific coherency matrices from canonical targets and a few matrices extracted from different land-cover types from full-polarimetric SAR images. We then apply the proposed method over diverse land-cover types using two full-polarimetric SAR images. We compare the results with the state-of-the-art three-component model-based decomposition techniques to validate the effectiveness of the proposed method that deals with the existing challenges of the model-based decomposition methods.
Volume
60
Subjects