A temporal phase coherence estimation algorithm and its application on DInSAR pixel selection

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Pixel selection is a crucial step of all advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques that have a direct impact on the quality of the final DInSAR products. In this paper, a full-resolution phase quality estimator, i.e., the temporal phase coherence (TPC), is proposed for DInSAR pixel selection. The method is able to work with both distributed scatterers (DSs) and permanent scatterers (PSs). The influence of different neighboring window sizes and types of interferograms combinations [both the single-master (SM) and the multi-master (MM)] on TPC has been studied. The relationship between TPC and phase standard deviation (STD) of the selected pixels has also been derived. Together with the classical coherence and amplitude dispersion methods, the TPC pixel selection algorithm has been tested on 37 VV polarization Radarsat-2 images of Barcelona Airport. Results show the feasibility and effectiveness of TPC pixel selection algorithm. Besides obvious improvements in the number of selected pixels, the new method shows some other advantages comparing with the other classical two. The proposed pixel selection algorithm, which presents an affordable computational cost, is easy to be implemented and incorporated into any advanced DInSAR processing chain for high-quality pixels' identification.Peer ReviewedPostprint (author's final draft

Coherency Matrix Decomposition-Based Polarimetric Persistent Scatterer Interferometry

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The rationale of polarimetric optimization techniques is to enhance the phase quality of the interferograms by combining adequately the different polarization channels available to produce an improved one. Different approaches have been proposed for polarimetric persistent scatterer interferometry (PolPSI). They range from the simple and computationally efficient BEST, where, for each pixel, the polarimetric channel with the best response in terms of phase quality is selected, to those with high-computational burden like the equal scattering mechanism (ESM) and the suboptimum scattering mechanism (SOM). BEST is fast and simple, but it does not fully exploit the potentials of polarimetry. On the other side, ESM explores all the space of solutions and finds the optimal one but with a very high-computational burden. A new PolPSI algorithm, named coherency matrix decomposition-based PolPSI (CMD-PolPSI), is proposed to achieve a compromise between phase optimization and computational cost. Its core idea is utilizing the polarimetric synthetic aperture radar (PolSAR) coherency matrix decomposition to determine the optimal polarization channel for each pixel. Three different PolSAR image sets of both full- (Barcelona) and dual-polarization (Murcia and Mexico City) are used to evaluate the performance of CMD-PolPSI. The results show that CMD-PolPSI presents better optimization results than the BEST method by using either $D_{\mathrm{ A}}$ or temporal mean coherence as phase quality metrics. Compared with the ESM algorithm, CMD-PolPSI is 255 times faster but its performance is not optimal. The influence of the number of available polarization channels and pixel's resolutions on the CMD-PolPSI performance is also discussed.Peer ReviewedPostprint (author's final draft

Advanced pixel selection and optimization algorithms for Persistent Scatterer Interferometry (PSI)

Tesi amb diferents seccions retallades per dret de l'editorPremi Extraordinari de Doctorat, promoció 2018-2019. Àmbit de les TICGround deformation measurements can provide valuable information for minimization of associated loss and damage caused by natural and environmental hazards. As a kind of remote sensing technique, Persistent Scatterer Interferometry (PSI) SAR is able to measure ground deformation with high spatial resolution, efficiently. Moreover, the ground deformation monitoring accuracy of PSI techniques can reach up to millimeter level. However, low coherence could hinderthe exploitation of SAR data, and high-accuracy deformation monitoring can only be achieved by PSI for high quality pixels. Therefore, pixel optimization and identification of coherent pixels are crucial for PSI techniques. In this thesis, advanced pixel selection and optimization algorithms have been investigated. Firstly, a full-resolution pixel selection method based on the Temporal Phase Coherence (TPC) has been proposed. This method first estimates noise phase term of each pixel at interferogram level. Then, for each pixel, its noise phase terms of all interferograms are used to assess this pixel’s temporal phase quality (i.e., TPC). In the next, based on the relationship between TPC and phase Standard Deviation (STD), a threshold can be posed on TPC to identify high phase quality pixels. This pixel selection method can work with both Deterministic Scatterers (PSs) and Distributed Scatterers (DSs). To valid the effectiveness of the developed method, it has been used to monitor the Canillo (Andorra) landslide. The results show that the TPC method can obtained highest density of valid pixels among the employed three approaches in this challenging area with X-band SAR data. Second, to balance the polarimetric DInSAR phase optimization effect and the computation cost, a new PolPSI algorithm is developed. This proposed PolPSI algorithm is based on the Coherency Matrix Decomposition result to determine the optimal scattering mechanism of each pixel, thus it is named as CMD-PolPSI. CMDPolPSI need not to search for solution within the full space of solution, it is therefore much computationally faster than the classical Equal Scattering Mechanism (ESM) method, but with lower optimization performance. On the other hand, its optimization performance outperforms the less computational costly BEST method. Third, an adaptive algorithm SMF-POLOPT has been proposed to adaptive filtering and optimizing PolSAR pixels for PolPSI applications. This proposed algorithm is based on PolSAR classification results to firstly identify Polarimetric Homogeneous Pixels (PHPs) for each pixel, and at the same time classify PS and DS pixels. After that, DS pixels are filtered by their associated PHPs, and then optimized based on the coherence stability phase quality metric; PS pixels are unfiltered and directly optimized based on the DA phase quality metric. SMF-POLOPT can simultaneously reduce speckle noise and retain structures’ details. Meanwhile, SMF-POLOPT is able to obtain much higher density of valid pixels for deformation monitoring than the ESM method. To conclude, one pixel selection method has been developed and tested, two PolPSI algorithms have been proposed in this thesis. This work make contributions to the research of “Advanced Pixel Selection and Optimization Algorithms for Persistent Scatterer InterferometryLes mesures de deformació del sòl poden proporcionar informació valuosa per minimitzar les pèrdues i els danys associats causats pels riscos naturals i ambientals. Com a tècnica de teledetecció, la interferometria de dispersors persistents (Persistent Scatter Interferometry, PSI) SAR és capaç de mesurar de forma eficient la deformació del terreny amb una alta resolució espacial. A més, la precisió de monitorització de la deformació del sòl de les tècniques PSI pot arribar a arribar a nivells del mil·límetre. No obstant això, una baixa coherència pot dificultar l’explotació de dades SAR i el control de deformació d’alta precisió només es pot aconseguir mitjançant PSI per a píxels d’alta qualitat. Per tant, l’optimització de píxels i la identificació de píxels coherents són crucials en les tècniques PSI. En aquesta tesi s¿han investigat algorismes avançats de selecció i optimització de píxels. En primer lloc, s'ha proposat un mètode de selecció de píxels de resolució completa basat en la coherència temporal de fase (Temporal Phase Coherence, TPC). Aquest mètode estima per primera vegada el terme de fase de soroll de cada píxel a nivell d’interferograma. A continuació, per a cada píxel, s'utilitzen els termes de la fase de soroll de tots els interferogrames per avaluar la qualitat de fase temporal d'aquest píxel (és a dir, TPC). A la següent, basant-se en la relació entre el TPC i la desviació estàndard de fase (STD), es pot plantejar un llindar de TPC per identificar píxels de qualitat de fase alta. Aquest mètode de selecció de píxels es capaç de detectar tant els dispersors deterministes (PS) com els distribuïts (DS). Per validar l’eficàcia del mètode desenvolupat, s’ha utilitzat per controlar l’esllavissada de Canillo (Andorra). Els resultats mostren que el mètode TPC pot obtenir la major densitat de píxels vàlids, comparat amb els mètodes clàssics de selecció, en aquesta àrea difícil amb dades de SAR de banda X. En segon lloc, per equilibrar l’efecte d’optimització de fase DInSAR polarimètrica i el cost de càlcul, es desenvolupa un nou algorisme de PolPSI. Aquest algorisme proposat de PolPSI es basa en el resultat de la descomposició de la matriu de coherència per determinar el mecanisme de dispersió òptim de cada píxel, de manera que es denomina CMD-PolPSI. CMDPolPSI no necessita buscar solucions dins de l’espai complet de la solució, per tant, és molt més eficient computacionalment que el mètode clàssic de mecanismes d’igualtat de dispersió (Equal Scattering Mechanism, ESM), però amb un efecte d’optimització no tant òptim. D'altra banda, el seu efecte d'optimització supera el mètode BEST, el que te un menor cost computacional. En tercer lloc, s'ha proposat un algoritme adaptatiu SMF-POLOPT per al filtratge adaptatiu i l'optimització de píxels PolSAR per a aplicacions PolPSI. Aquest algorisme proposat es basa en els resultats de classificació PolSAR per identificar primer els píxels homogenis polarimètrics (PHP) per a cada píxel i, alhora, classificar els píxels PS i DS. Després d'això, els píxels DS es filtren pels seus PHP associats i, a continuació, s'optimitzen en funció de la mètrica de qualitat de la fase d'estabilitat de coherència; els píxels classificats com PS no es filtren i s'optimitzen directament en funció de la mètrica de qualitat de la fase DA. SMF-POLOPT pot reduir simultàniament el soroll de la fase interferomètrica i conservar els detalls de les estructures. Mentrestant, SMF-POLOPT aconsegueix obtenir una densitat molt més alta de píxels vàlids per al seguiment de la deformació que el mètode ESM. Per concloure, en aquesta tesi s’ha desenvolupat i provat un mètode de selecció de píxels, i s’han proposat dos algoritmes PolPSI. Aquest treball contribueix a la recerca en "Advanced Pixel Selection and Optimization Algorithms for Persistent Scatterer Interferometry"Postprint (published version

Post-failure evolution analysis of a rainfall-triggered landslide by multi-temporal interferometry SAR approaches integrated with geotechnical analysis

Persistent Scatterers Interferometry (PSI) represents one of the most powerful techniques for Earth's surface deformation processes' monitoring, especially for long-term evolution phenomena. In this work, a dataset of 34 TerraSAR-X StripMap images (October 2013–October 2014) has been processed by two PSI techniques - Coherent Pixel Technique-Temporal Sublook Coherence (CPT-TSC) and Small Baseline Subset (SBAS) - in order to study the evolution of a slow-moving landslide which occurred on February 23, 2012 in the Papanice hamlet (Crotone municipality, southern Italy) and induced by a significant rainfall event (185 mm in three days). The mass movement caused structural damage (buildings' collapse), and destruction of utility lines (gas, water and electricity) and roads. The results showed analogous displacement rates (30–40 mm/yr along the Line of Sight – LOS-of the satellite) with respect to the pre-failure phase (2008–2010) analyzed in previous works. Both approaches allowed detect the landslide-affected area, however the higher density of targets identified by means of CPT-TSC enabled to analyze in detail the slope behavior in order to design possible mitigation interventions. For this aim, a slope stability analysis has been carried out, considering the comparison between groundwater oscillations and time-series of displacement. Hence, the crucial role of the interaction between rainfall and groundwater level has been inferred for the landslide triggering. In conclusion, we showed that the integration of geotechnical and remote sensing approaches can be seen as the best practice to support stakeholders to design remedial works.Peer ReviewedPostprint (author's final draft

Landslide monitoring using multi-temporal SAR interferometry with advanced persistent scatterers identification methods and super high-spatial resolution terraSAR-X Images

Landslides are one of the most common and dangerous threats in the world that generate considerable damage and economic losses. An efficient landslide monitoring tool is the Differential Synthetic Aperture Radar Interferometry (DInSAR) or Persistent Scatter Interferometry (PSI). However, landslides are usually located in mountainous areas and the area of interest can be partially or even heavily vegetated. The inherent temporal decorrelation that dramatically reduces the number of Persistent Scatters (PSs) of the scene limits in practice the application of this technique. Thus, it is crucial to be able to detect as much PSs as possible that can be usually embedded in decorrelated areas. High resolution imagery combined with efficient pixel selection methods can make possible the application of DInSAR techniques in landslide monitoring. In this paper, different strategies to identify PS Candidates (PSCs) have been employed together with 32 super high-spatial resolution (SHR) TerraSAR-X (TSX) images, staring-spotlight mode, to monitor the Canillo landslide (Andorra). The results show that advanced PSI strategies (i.e., the temporal sub-look coherence (TSC) and temporal phase coherence (TPC) methods) are able to obtain much more valid PSs than the classical amplitude dispersion (DA) method. In addition, the TPC method presents the best performance among all three full-resolution strategies employed. The SHR TSX data allows for obtaining much higher densities of PSs compared with a lower-spatial resolution SAR data set (Sentinel-1A in this study). Thanks to the huge amount of valid PSs obtained by the TPC method with SHR TSX images, the complexity of the structure of the Canillo landslide has been highlighted and three different slide units have been identified. The results of this study indicate that the TPC approach together with SHR SAR images can be a powerful tool to characterize displacement rates and extension of complex landslides in challenging areasPeer ReviewedPostprint (published version

Application of Differential and Polarimetric Synthetic Aperture Radar (SAR) Interferometry for Studying Natural Hazards

In the following work, I address the problem of coherence loss in standard Differential Interferometric SAR (DInSAR) processing, which can result in incomplete or poor quality deformation measurements in some areas. I incorporate polarimetric information with DInSAR in a technique called Polarimetric SAR Interferometry (PolInSAR) in order to acquire more accurate and detailed maps of surface deformation. In Chapter 2, I present a standard DInSAR study of the Ahar double earthquakes (Mw=6.4 and 6.2) which occurred in northwest Iran, August 11, 2012. The DInSAR coseismic deformation map was affected by decorrelation noise. Despite this, I employed an advanced inversion technique, in combination with a Coulomb stress analysis, to find the geometry and the slip distribution on the ruptured fault plane. The analysis shows that the two earthquakes most likely occurred on a single fault, not on conjugate fault planes. This further implies that the minor strike-slip faults play more significant role in accommodating convergence stress accumulation in the northwest part of Iran. Chapter 3 presents results from the application of PolInSAR coherence optimization on quad-pol RADARSAT-2 images. The optimized solution results in the identification of a larger number of reliable measurement points, which otherwise are not recognized by the standard DInSAR technique. I further assess the quality of the optimized interferometric phase, which demonstrates an increased phase quality with respect to those phases recovered by applying standard DInSAR alone. Chapter 4 discusses results from the application of PolInSAR coherence optimization from different geometries to the study of creep on the Hayward fault and landslide motions near Berkeley, CA. The results show that the deformation rates resolved by PolInSAR are in agreement with those of standard DInSAR. I also infer that there is potential motion on a secondary fault, northeast and parallel to the Hayward fault, which may be creeping with a lower velocity

SMF-POLOPT: an adaptive multitemporal pol(DIn)SAR filtering and phase optimization algorithm for PSI applications

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Speckle noise and decorrelation can hamper the application and interpretation of PolSAR images. In this paper, a new adaptive multitemporal 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 polarimetric DInSAR (POLDInSAR) 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. Thirty-one 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 full-polarization case) and ×3.8 (the dual-polarization case) with respect to 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.Peer ReviewedPostprint (author's final draft

Sentinel-1 data exploitation for terrain deformation monitoring

Persistent Scatterer interferometry (PSI) is a group of advanced differential interferometric Synthetic Aperture Radar (SAR) techniques used to measure and monitor terrain deformation. Sentinel-1 has improved the data acquisition throughout and, compared to previous sensors, increased considerably the Differential Interferometric SAR (DInSAR) and PSI deformation monitoring potential. The effect of the refractive atmosphere on the interferometric phase and phase unwrapping ambiguity are two critical issues of InSAR. The low density of Persistent Scatterer (PS) in non-urban areas, another critical issue, has inspired the development of alternative approaches and refinement of the PS chains. Along with the efforts to develop methods to mitigate the three above-mentioned problems, the work presented in this thesis also deals with the presence of a new signal in multilooked interferograms which cannot be explained by noise, atmospheric or earth surface topography changes. This paper describes a method for atmospheric phase screen estimation using rain station weather data and three different data driven procedures to obtain terrain deformation maps. These approaches aim to exploit Sentinel-1 highly coherent interferograms and their short revisit time. The first method called the splitting makes uses of the power spectrum of the interferograms to split the signals into high and low frequency, and following a mutually exclusive consecutive processing chain for the two sets. This approach has resulted in greater density of PSs with decreased phase unwrapping errors. The second approach, called Direct Integration (DI), aims at providing a very fast and straightforward approach to screen wide areas and easily detect active areas. This approach fully exploits the coherent interferograms from the consecutive images provided by Sentinel-1 resulting in a very high sampling density. However, it lacks robustness and its usability lays on the operator experience. The third method, called PSIG (Persistent Scatterer Interferometry Geomatics) short temporal baseline, provides a constrained application of the PSIG chain, the CTTC approach to the PSI. It uses short temporal baseline interferograms and do not assume any deformation model for point selection. It is also quite a straightforward approach and a perfect complement to the direct integration approach. It improves the performances of the standard PSIG approach, increasing the PS density and providing robust measurements. The effectiveness of the approaches is illustrated through analyses performed on different test sites.La técnica Persistent Scatterer Interferometry (PSI) es un grupo de técnicas avanzadas de radar de apertura sintética interferométrica diferencial (SAR) que se utiliza para medir y monitorear losmovimientos del terreno. Sentinel-1 ha mejorado sensiblemente la adquisición de datos y, en comparación con los sensores SAR anteriores, ha aumentado considerablemente el potencial uso de la interferometría diferencial SAR y del PSI para medir y monitorizar desplazamientos del terreno. El efecto de la atmósfera sobre la fase interferométrica y la naturaleza ambigua de esta son dos cuestiones críticas de InSAR. Además, la baja densidad de Persistent Scatterer (PSs) en áreas no urbanas, es otro tema crítico que ha inspirado el desarrollo de enfoques alternativos y el refinamiento de las cadenas PS existentes. Junto con los esfuerzos por desarrollar métodos para mitigar los tres problemas antes mencionados, el trabajo presentado en esta tesis también aborda la presencia de una nueva señal en interferogramas multilooked que no puede explicarse por cambios de ruido, atmosféricos o topográficos de la superficie terrestre. Esta tesis describe un método para la estimación de la fase atmosférica utilizando datos meteorológicos adquiridos in-situ y tres aproximaciones diferentes basadas en datos Sentinel-1 para obtener mapas de deformación del terreno. Estos enfoques tienen como objetivo explotar los interferogramas altamente coherentes proporcionados por Sentinel-1 gracias a su corto tiempo de revisita. El primer método llamado división hace uso de filtros en el dominico frecuencial de los interferogramas para dividir las señales en alta y baja frecuencia, y siguiendo una cadena de procesamiento consecutiva independiente para cada clase. Este enfoque ha dado como resultado una mejora substancial de PS minimizando los errores debidos al desenrollado de fase. El segundo enfoque, llamado Integración Directa (DI), tiene como objetivo proporcionar un enfoque muy rápido y sencillo para examinar áreas amplias y detectar fácilmente áreas activas. Este enfoque aprovecha al máximo los interferogramas coherentes de las imágenes consecutivas proporcionadas por Sentinel-1, lo que da como resultado una densidad de muestreo muy alta. Sin embargo, carece de robustez y su usabilidad depende de la experiencia del operador. El tercer método, llamado PSIG (Persistent Scatterer Interferometry Geomatics) de línea de base temporal corta, proporciona una aplicación restringida de la cadena PSIG, el enfoque CTTC para el PSI. Utiliza interferogramas de línea base temporales cortos y no asume ningún modelo de deformación para la selección de puntos. Su uso es complementario al enfoque de integración directa proporcionando robustez en las zonas. Mejora el rendimiento del enfoque estándar de PSIG, aumentando la densidad de PS y proporcionando mediciones robustas. La efectividad de los enfoques se ilustra a través de análisis realizados en diferentes sitios de prueba.Postprint (published version

Spaceborne Differential SAR Interferometry: Data Analysis Tools for Deformation Measurement

This paper is focused on spaceborne Differential Interferometric SAR (DInSAR) for land deformation measurement and monitoring. In the last two decades several DInSAR data analysis procedures have been proposed. The objective of this paper is to describe the DInSAR data processing and analysis tools developed at the Institute of Geomatics in almost ten years of research activities. Four main DInSAR analysis procedures are described, which range from the standard DInSAR analysis based on a single interferogram to more advanced Persistent Scatterer Interferometry (PSI) approaches. These different procedures guarantee a sufficient flexibility in DInSAR data processing. In order to provide a technical insight into these analysis procedures, a whole section discusses their main data processing and analysis steps, especially those needed in PSI analyses. A specific section is devoted to the core of our PSI analysis tools: the so-called 2+1D phase unwrapping procedure, which couples a 2D phase unwrapping, performed interferogram-wise, with a kind of 1D phase unwrapping along time, performed pixel-wise. In the last part of the paper, some examples of DInSAR results are discussed, which were derived by standard DInSAR or PSI analyses. Most of these results were derived from X-band SAR data coming from the TerraSAR-X and CosmoSkyMed sensors

Generation of Earth’s Surface Three-Dimensional (3-D) Displacement Time-Series by Multiple-Platform SAR Data

In this chapter, the recent advancements of differential synthetic aperture radar interferometry (DInSAR) technique are presented, with the focus on the DInSAR-based approaches leading to the generation of three-dimensional time-series of Earth’s surface deformation, based on the combination of multi-platform line-of-sight (LOS)-projected time-series of deformation. Use of pixel-offset (PO) measurements for the retrieval of North-South deformation components, which are difficult to be extracted from DInSAR data, only, is also discussed. A review of the principal techniques based on the exploitation of amplitude and phase signatures of sequences of SAR images will be first provided, by emphasizing the limitations and strength of each single approach. Then, the interest will be concentrated on the recently proposed multi-track InSAR combination algorithm, referred as minimum acceleration InSAR combination (MinA) approach. The algorithm assumes the availability of two (or more) sets of SAR images acquired from complementary tracks. SAR data are pre-processed through one of currently available multi-temporal DInSAR toolboxes, and the LOS-projected surface deformation time-series are computed. An under-determined system of linear equations is then solved, based on imposing that the 3-D displacement time-series have minimum acceleration (MA). The presented results demonstrate the validity of the MinA algorithm