A phase-decomposition-based polarimetric coherence optimization method

Abstract

The interference between different scattering components in distributed scatterer (DS) makes it difficult to estimate the phase from DS. Recently, some methods based on eigenvalue decomposition (EVD) are proposed to separate scatterer components from DS, such as component extraction and selection SAR (CAESAR) and phase-decomposition-based persistent scatterer (PS) InSAR (PD-PSInSAR). Meanwhile, many polarimetric radar satellites launched in recent years provide abundant polarization data, which promotes the research of polarimetric coherence optimization (PCO). The capability of PCO to improve the amount and quality of PSs has been verified. In this paper, a phase-decomposition-based polarimetric coherence optimization (PD-PCO) is proposed to explore the potential of PCO to improve the phase quality of DS. Firstly, the contribution of the first principal component on a coherence matrix is obtained by searching for the optimum projection vector. Then, the new scattering coefficient is generated by combining the polarimetric scattering vector and the projection vector. Finally, the interferometric phase of dominant scattering mechanism component is separated from the phase component by EVD. Twenty-eight dual-pol Sentinel-1A SAR images have been adopted to validate the effectiveness of the proposed method. Phase derivative variation and coherence of the proposed method has been compared with other algorithms. The result shows that the quality of phase estimated by the PD-PCO is superior to the CAESAR, PD-PSInSAR, and the mean coherence polarimetric optimization based on equivalent scattering mechanism (ESM-R). The experimental results show that the proposed algorithm successfully improves the phase quality of DSCs.