SMF-POLOPT: An Adaptive Multitemporal Pol(DIn)SAR Filtering and Phase Optimization Algorithm for PSI Applications

Abstract

Speckle noise and decorrelation can hamper the application and interpretation of PolSAR images. In this paper, a new adaptive multi-temporal Pol(DIn)SAR filtering and phase optimization algorithm is proposed to address these limitations. This algorithm first categorizes and adaptively filters permanent scatterer (PS) and distributed scatterer (DS) pixels according to their polarimetric scattering mechanisms (i.e. the Scattering-Mechanism based Filtering (SMF)). Then two different POLDIn-SAR phase OPTimization methods are applied separately on the filtered PS and DS pixels (i.e. POLOPT). Finally, an inclusive pixel selection approach is used to identify high quality pixels for ground deformation estimation. 31 full-polarization Radarsat-2 SAR images over Barcelona (Spain) and 31 dual-polarization TerraSAR-X images over Murcia (Spain) have been used to evaluate the performance of the proposed algorithm. The PolSAR filtering results show that the speckle of PolSAR images has been well reduced with the preservation of details by the proposed SMF. The obtained ground deformation monitoring results have shown significant improvements, about ×7.2 (the fullpolarization case) and ×3.8 (the dual-polarization case) w.r.t. the classical full-resolution single-pol amplitude dispersion method, on the valid pixels' densities. The excellent PolSAR filtering and ground deformation monitoring results achieved by the adaptive Pol(DIn)SAR filtering and phase optimization algorithm (i.e. the SMF-POLOPT) have validated the effectiveness of this proposed scheme.

Polarimetric Persistent Scatterer Interferometry for Ground Deformation Monitoring with VV-VH Sentinel-1 Data

“…As more and more sattelites with the capability of acquiring PolSAR images (e.g., Radarsat-2, ALOS-2, TerraSAR-X, and Sentinel-1) have been launched, it is possible to extend the PolPSI techniques from the Ground-based SAR images to satellite SAR images. Under this circumstance, more PolPSI algorithms have been proposed [28][29][30][31][32][33][34][35][36][37][38][39].…”

“…Generally, like the conventional PSI methods, the amplitude dispersion index (ADI, better for PS pixels' optimization) [26,33,36] and coherence (better for DS pixel optimization) [25,28,29,36] are the two commonly used phase quality metrics. Similarly with DS-InSAR, more advanced PolPSI techniques are proposed to adaptive optimize PS and DS pixels by using the combination of ADI and coherence [31,34,37,38]. Therefore, according to the phase quality metrics used, the most commonly used PolPSI techniques can be classified into three main categories, i.e., PolPSI-ADI (PolPSI with the amplitude dispersion index as the phase quality metric) [26,33,36], PolPSI-COH (PolPSI with the coherence as the phase quality metric) [25,28,29,36], and PolPSI-AOS (PolPSI with the adaptive optimization strategies) [31,34,37,38].…”

“…Similarly with DS-InSAR, more advanced PolPSI techniques are proposed to adaptive optimize PS and DS pixels by using the combination of ADI and coherence [31,34,37,38]. Therefore, according to the phase quality metrics used, the most commonly used PolPSI techniques can be classified into three main categories, i.e., PolPSI-ADI (PolPSI with the amplitude dispersion index as the phase quality metric) [26,33,36], PolPSI-COH (PolPSI with the coherence as the phase quality metric) [25,28,29,36], and PolPSI-AOS (PolPSI with the adaptive optimization strategies) [31,34,37,38]. Different PolPSI algorithms are with different phase optimization performances, and suitable for different scenarios [36,37].…”

“…Therefore, according to the phase quality metrics used, the most commonly used PolPSI techniques can be classified into three main categories, i.e., PolPSI-ADI (PolPSI with the amplitude dispersion index as the phase quality metric) [26,33,36], PolPSI-COH (PolPSI with the coherence as the phase quality metric) [25,28,29,36], and PolPSI-AOS (PolPSI with the adaptive optimization strategies) [31,34,37,38]. Different PolPSI algorithms are with different phase optimization performances, and suitable for different scenarios [36,37]. Although it is possible to apply PolPSI techniques on satellite SAR images for ground deformation monitoring over broader areas, the free available time-series PolSAR images are far away from enough for PolPSI applications.…”

Impact of SAR Image Resolution on Polarimetric Persistent Scatterer Interferometry With Amplitude Dispersion Optimization

“…As more and more satellites with the capability of acquiring polarimetric data have been deployed, it is possible to extend the PSI algorithms into the polarimetric domain. Under these circumstances, the polarimetric persistent scatterers interferometry SAR (PolPSI) has been proposed [21]- [32]. In PolPSI, two-phase quality criteria are commonly used for pixels' optimization.…”