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

Transient displacement analysis using double-pulsed ESPI and fringe processing methods

This thesis deals with techniques for the displacement measurement of fast transient phenomena using ESPI. Four main contributions are presented. First, a computer model for speckle noise and ESPI fringe generation is proposed. An assessment methodology for speckle noise reduction algorithms is then derived using the computer model. Then the noise in the ESPI fringe patterns is analysed using computer generated speckle and several solutions for its reduction are proposed and assessed. Finally, a fast electro-optical system is presented as a solution to the unambiguous phase extraction problem from a single interferogram. With this novel system, whole field transient displacements occurring in time intervals as short as 20ns can be successfully registered and retrieved. [Continues.

INSAR Principles B

reserved5A. Ferretti; A. Monti Guarnieri; C. Prati; F. Rocca; D. MassonnetFerretti, Alessandro; MONTI-GUARNIERI, ANDREA VIRGILIO; Prati, CLAUDIO MARIA; Rocca, Fabio; D., Massonne

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

Temporal Coherence Estimators for GBSAR

Many Ground-Based Synthetic Aperture Radar (GBSAR) applications demand preliminary analysis to select areas with high-quality signal. That is, areas in which the phase can be processed to extract the desired information. The interferometric coherence and the amplitude dispersion index are important tools widely used in the literature to assess the quality of GBSAR images. So far, no direct relation has been found between the two. Indeed, they are parameters of different natures: amplitude dispersion index is calculated with only amplitude values, while coherence provides information also on the signal phase. The purpose of this article is to find a relation between the two parameters. Indeed, the amplitude dispersion index provides some practical advantages if compared to coherence estimators, especially to perform fast preliminary analysis. In this article, a theoretical relation between amplitude dispersion index and coherence is retrieved. GBSAR measurements acquired in different scenarios, at different working frequencies are presented and used to validate such a relation

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

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