An Adaptive Decomposition Approach with Dipole Aggregation Model for Polarimetric SAR Data

Abstract

Polarimetric synthetic aperture radar (PolSAR) has attracted lots of attention from remote sensing scientists because of its various advantages, e.g., all-weather, all-time, penetrating capability, and multi-polarimetry. The three-component scattering model proposed by Freeman and Durden (FDD) has bridged the data and observed target with physical scattering model, whose simplicity and practicality have advanced remote sensing applications. However, the three-component scattering model also has some disadvantages, such as negative powers and a scattering model unfitted to observed target, which can be improved by adaptive methods. In this paper, we propose a novel adaptive decomposition approach in which we established a dipole aggregation model to fit every pixel in PolSAR image to an independent volume scattering mechanism, resulting in a reduction of negative powers and an improved adaptive capability of decomposition models. Compared with existing adaptive methods, the proposed approach is fast because it does not utilize any time-consuming algorithm of iterative optimization, is simple because it does not complicate the original three-component scattering model, and is clear for each model being fitted to explicit physical meaning, i.e., the determined adaptive parameter responds to the scattering mechanism of observed target. The simulation results indicated that this novel approach reduced the possibility of the occurrence of negative powers. The experiments on ALOS-2 and RADARSAT-2 PolSAR images showed that the increasing of adaptive parameter reflected more effective scatterers aggregating at the 45° direction corresponding to high cross-polarized property, which always appeared in the 45° oriented buildings. Moreover, the random volume scattering model used in the FDD could be expressed by the novel dipole aggregation model with an adaptive parameter equal to one that always appeared in the forest area.