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

Non-local methods for InSAR parameters estimation

In the thesis work the nonlocal paradigm has been investigated in the framework of Multitemporal SAR Interferometry, e.g. Differential Interferometry, Tomography, etc., and single InSAR pair, e.g. DEM generation. In the former, Adaptive Multi-Looking methods have been developed for the generation of interferometric data-stacks. Following the nonlocal approach, the proposed methods rely only on similar pixels according to a suitable similarity measure that exploits the stack's temporal information. An hybrid approach that jointly uses the nonlocal paradigm and transform domain filtering has been investigated for InSAR pair phase estimation. On the track of the BM3D and SARBM3D algorithms, different approaches to the filtering in the transform domain are investigated. Furthermore, a novel approach to the similarity computation and filtering, based on a relative-topography content of the interferometric phase rather than its absolute value, is proposed

Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

The automatic detection of landslides after major events is a crucial issue for public agencies to support disaster response. Pixel-based approaches (PBAs) are widely used in the literature for various applications. However, the accuracy of PBAs in the case of automatic landslide mapping (ALM) is affected by several issues. In this study, we investigated the sensitivity of ALM using PBA through digital terrain models (DTMs). The analysis, carried out in a study area of Poland, consisted of the following steps: (1) testing the influence of selected DTM resolutions for ALM, (2) assessing the relevance of diverse landslide morphological indicators for ALM, and (3) assessing the sensitivity to landslide features for a selected size of moving window (kernel) calculations for ALM. Ultimately, we assessed the performance of three classification methods: maximum likelihood (ML), feed-forward neural network (FFNN), and support vector machine (SVM). This broad analysis, as combination of grid cell resolution, surface derivatives calculation, and performance classification methods, is the challenging aspect of the research. The results of almost 500 experimental tests provide valuable guidelines for experts performing ALM. The most important findings indicate that feature sensitivity in the case of kernel size increases with coarser DTM resolution; however, the peak of the optimal feature performance for the selected study area and landslide type was demonstrated for a resolution of 20 m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30 m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification

Radar satellite imagery for humanitarian response. Bridging the gap between technology and application

This work deals with radar satellite imagery and its potential to assist of humanitarian operations. As the number of displaced people annually increases, both hosting countries and relief organizations face new challenges which are often related to unclear situations and lack of information on the number and location of people in need, as well as their environments. It was demonstrated in numerous studies that methods of earth observation can deliver this important information for the management of crises, the organization of refugee camps, and the mapping of environmental resources and natural hazards. However, most of these studies make use of -high-resolution optical imagery, while the role of radar satellites is widely neglected. At the same time, radar sensors have characteristics which make them highly suitable for humanitarian response, their potential to capture images through cloud cover and at night in the first place. Consequently, they potentially allow quicker response in cases of emergencies than optical imagery. This work demonstrates the currently unused potential of radar imagery for the assistance of humanitarian operations by case studies which cover the information needs of specific emergency situations. They are thematically grouped into topics related to population, natural hazards and the environment. Furthermore, the case studies address different levels of scientific objectives: The main intention is the development of innovative techniques of digital image processing and geospatial analysis as an answer on the identified existing research gaps. For this reason, novel approaches are presented on the mapping of refugee camps and urban areas, the allocation of biomass and environmental impact assessment. Secondly, existing methods developed for radar imagery are applied, refined, or adapted to specifically demonstrate their benefit in a humanitarian context. This is done for the monitoring of camp growth, the assessment of damages in cities affected by civil war, and the derivation of areas vulnerable to flooding or sea-surface changes. Lastly, to foster the integration of radar images into existing operational workflows of humanitarian data analysis, technically simple and easily-adaptable approaches are suggested for the mapping of rural areas for vaccination campaigns, the identification of changes within and around refugee camps, and the assessment of suitable locations for groundwater drillings. While the studies provide different levels of technical complexity and novelty, they all show that radar imagery can largely contribute to the provision of a variety of information which is required to make solid decisions and to effectively provide help in humanitarian operations. This work furthermore demonstrates that radar images are more than just an alternative image source for areas heavily affected by cloud cover. In fact, what makes them valuable is their information content regarding the characteristics of surfaces, such as shape, orientation, roughness, size, height, moisture, or conductivity. All these give decisive insights about man-made and natural environments in emergency situations and cannot be provided by optical images Finally, the findings of the case studies are put into a larger context, discussing the observed potential and limitations of the presented approaches. The major challenges are summarized which need be addressed to make radar imagery more useful in humanitarian operations in the context of upcoming technical developments. New radar satellites and technological progress in the fields of machine learning and cloud computing will bring new opportunities. At the same time, this work demonstrated the large need for further research, as well as for the collaboration and transfer of knowledge and experiences between scientists, users and relief workers in the field. It is the first extensive scientific compilation of this topic and the first step for a sustainable integration of radar imagery into operational frameworks to assist humanitarian work and to contribute to a more efficient provision of help to those in need.Die vorliegende Arbeit beschäftigt sich mit bildgebenden Radarsatelliten und ihrem potenziellen Beitrag zur Unterstützung humanitärer Einsätze. Die jährlich zunehmende Zahl an vertriebenen oder geflüchteten Menschen stellt sowohl Aufnahmeländer als auch humanitäre Organisationen vor große Herausforderungen, da sie oft mit unübersichtlichen Verhältnissen konfrontiert sind. Effektives Krisenmanagement, die Planung und Versorgung von Flüchtlingslagern, sowie der Schutz der betroffenen Menschen erfordern jedoch verlässliche Angaben über Anzahl und Aufenthaltsort der Geflüchteten und ihrer natürlichen Umwelt. Die Bereitstellung dieser Informationen durch Satellitenbilder wurde bereits in zahlreichen Studien aufgezeigt. Sie beruhen in der Regel auf hochaufgelösten optischen Aufnahmen, während bildgebende Radarsatelliten bisher kaum Anwendung finden. Dabei verfügen gerade Radarsatelliten über Eigenschaften, die hilfreich für humanitäre Einsätze sein können, allen voran ihre Unabhängigkeit von Bewölkung oder Tageslicht. Dadurch ermöglichen sie in Krisenfällen verglichen mit optischen Satelliten eine schnellere Reaktion. Diese Arbeit zeigt das derzeit noch ungenutzte Potenzial von Radardaten zur Unterstützung humanitärer Arbeit anhand von Fallstudien auf, in denen konkrete Informationen für ausgewählte Krisensituationen bereitgestellt werden. Sie sind in die Themenbereiche Bevölkerung, Naturgefahren und Ressourcen aufgeteilt, adressieren jedoch unterschiedliche wissenschaftliche Ansprüche: Der Hauptfokus der Arbeit liegt auf der Entwicklung von innovativen Methoden zur Verarbeitung von Radarbildern und räumlichen Daten als Antwort auf den identifizierten Forschungsbedarf in diesem Gebiet. Dies wird anhand der Kartierung von Flüchtlingslagern zur Abschätzung ihrer Bevölkerung, zur Bestimmung von Biomasse, sowie zur Ermittlung des Umwelteinflusses von Flüchtlingslagern aufgezeigt. Darüber hinaus werden existierende oder erprobte Ansätze für die Anwendung im humanitären Kontext angepasst oder weiterentwickelt. Dies erfolgt im Rahmen von Fallstudien zur Dynamik von Flüchtlingslagern, zur Ermittlung von Schäden an Gebäuden in Kriegsgebieten, sowie zur Erkennung von Risiken durch Überflutung. Zuletzt soll die Integration von Radardaten in bereits existierende Abläufe oder Arbeitsroutinen in der humanitären Hilfe anhand technisch vergleichsweise einfacher Ansätze vorgestellt und angeregt werden. Als Beispiele dienen hier die radargestützte Kartierung von entlegenen Gebieten zur Unterstützung von Impfkampagnen, die Identifizierung von Veränderungen in Flüchtlingslagern, sowie die Auswahl geeigneter Standorte zur Grundwasserentnahme. Obwohl sich die Fallstudien hinsichtlich ihres Innovations- und Komplexitätsgrads unterscheiden, zeigen sie alle den Mehrwert von Radardaten für die Bereitstellung von Informationen, um schnelle und fundierte Planungsentscheidungen zu unterstützen. Darüber hinaus wird in dieser Arbeit deutlich, dass Radardaten für humanitäre Zwecke mehr als nur eine Alternative in stark bewölkten Gebieten sind. Durch ihren Informationsgehalt zur Beschaffenheit von Oberflächen, beispielsweise hinsichtlich ihrer Rauigkeit, Feuchte, Form, Größe oder Höhe, sind sie optischen Daten überlegen und daher für viele Anwendungsbereiche im Kontext humanitärer Arbeit besonders. Die in den Fallstudien gewonnenen Erkenntnisse werden abschließend vor dem Hintergrund von Vor- und Nachteilen von Radardaten, sowie hinsichtlich zukünftiger Entwicklungen und Herausforderungen diskutiert. So versprechen neue Radarsatelliten und technologische Fortschritte im Bereich der Datenverarbeitung großes Potenzial. Gleichzeitig unterstreicht die Arbeit einen großen Bedarf an weiterer Forschung, sowie an Austausch und Zusammenarbeit zwischen Wissenschaftlern, Anwendern und Einsatzkräften vor Ort. Die vorliegende Arbeit ist die erste umfassende Darstellung und wissenschaftliche Aufarbeitung dieses Themenkomplexes. Sie soll als Grundstein für eine langfristige Integration von Radardaten in operationelle Abläufe dienen, um humanitäre Arbeit zu unterstützen und eine wirksame Hilfe für Menschen in Not ermöglichen

Atmospheric artifacts correction for InSAR using empirical model and numerical weather prediction models

lnSAR has been proved its unprecedented ability and merits of monitoring ground deformation on large scale with centimeter to millimeter scale accuracy. However, several factors affect the reliability and accuracy of its applications. Among them, atmospheric artifacts due to spatial and temporal variations of atmosphere state often pose noise to interferograms. Therefore, atmospheric artifacts m itigalion remains one of the biggest challenges to be addressed in the In SAR community. State-of-the-art research works have revealed atmospheric artifacts can be partially compensated with empirical models, temporal-spatial filtering approach in lnSAR time series, pointwise GPS zenith path delay and numerical weather prediction models. In this thesis, firstly, we further develop a covariance weighted linear empirical model correction method. Secondly, a realistic LOS direction integration approach based on global reanalysis data is employed and comprehensively compared with the conventional method that integrates along zenith direction. Finally, the realistic integration method is applied to local WRF numerical forecast model data. l'vbreover, detailed comparisons between different global reanalysis data and local WRF model are assessed. In terms of empirical models correcting methods, many publications have studied correcting stratified tropospheric phase delay by assuming a linear model between them and topography. However, most of these studies ha\19 not considered the effect of turbulent atmospheric artefacts when adjusting the linear model to data. In this thesis, an improved technique that minimizes the influence of turbulent atmosphere in the model adjustment has been presented. In the proposed algorithm, the model is adjusted to the phase differences of pixels instead of using the unwrapped phase of each pixel. In addition, the different phase differences are weighted as a function of its APS covariance estimated from an empirical variogram to reduce in the model adjustment the impact of pixel pairs with significant turbulent atmosphere. The performance of the proposed method has been validated with both simulated and real Sentinel-1 SAR data in Tenerife island, Spain. Considering methods using meteorological observations to mitigate APS, an accurate realistic com puling strategy utilizing global atmospheric reanalysis data has been implemented. With the approach, the realistic LOS path along satellite and the monitored points is considered, rather than converting from zenith path delay. Com pared with zenith delay based method, the biggest advantage is that it can avoid errors caused by anisotropic atmospheric behaviour. The accurate integration method is validated with Sentinel-1 data in three test sites: Tenerife island, Spain, Almeria, Spain and Crete island, Greece. Compared to conventional zenith method, the realistic integration method shows great improvement. A variety of global reanalysis data are available from different weather forecasting organizations, such as ERA-Interim, ERAS, MERRA2. In this study, the realistic integration mitigation method is assessed on these different reanalysis data. The results show that these data are feasible to mitigate APS to some extent in most cases. The assessment also demonstrates that the ERAS performs the best statistically, compared to other global reanalysis data. l'vbreover, as local numerical weather forecast models have the ability to predict high spatial resolution atmospheric parameters, by using which, it has the potential to achieve APS mitigation. In this thesis, the realistic integration method is also employed on the local WRF model data in Tenerife and Almeria test s ites. However, it turns out that the WRF model performs worse than the original global reanalysis data.Las técnicas lnSAR han demostrado su capacidad sin precedentes y méritos para el monitoreo de la deformaci6n del suelo a gran escala con una precisión centimétrica o incluso milimétrica. Sin embargo, varios factores afectan la fiabilidad y precisión de sus aplicaciones. Entre ellos, los artefactos atmosféricos debidos a variaciones espaciales y temporales del estado de la atm6sfera a menudo añaden ruido a los interferogramas. Por lo tanto, la mitigación de los artefactos atmosféricos sigue siendo uno de los mayores desafíos a abordar en la comunidad lnSAR. Los trabajos de investigaci6n de vanguardia han revelado que los artefactos atmosféricos se pueden compensar parcialmente con modelos empíricos, enfoque de filtrado temporal-espacial en series temporales lnSAR, retardo puntual del camino cenital con GPS y modelos numéricos de predicción meteorológica. En esta tesis, en primer lugar, desarrollamos un método de corrección de modelo empírico lineal ponderado por covarianza. En segundo lugar, se emplea un enfoque realista de integracion de dirección LOS basado en datos de reanálisis global y se compara exhaustivamente con el método convencional que se integra a lo largo de la dirección cenital. Finalmente, el método de integraci6n realista se aplica a los datos del modelo de pronóstico numérico WRF local. Ademas, se evalúan las comparaciones detalladas entre diferentes datos de reanálisis global y el modelo WRF local. En términos de métodos de corrección con modelos empíricos, muchas publicaciones han estudiado la corrección del retraso estratificado de la fase troposférica asumiendo un modelo lineal entre ellos y la topografía. Sin embargo, la mayoría de estos estudios no han considerado el efecto de los artefactos atmosféricos turbulentos al ajustar el modelo lineal a los datos. En esta tesis, se ha presentado una técnica mejorada que minimiza la influencia de la atm6sfera turbulenta en el ajuste del modelo. En el algoritmo propuesto, el modelo se ajusta a las diferencias de fase de los pixeles en lugar de utilizar la fase sin desenrollar de cada pixel. Además, las diferentes diferencias de fase se ponderan en función de su covarianza APS estimada a partir de un variograma empírico para reducir en el ajuste del modelo el impacto de los pares de pixeles con una atm6sfera turbulenta significativa. El rendimiento del método propuesto ha sido validado con datos SAR Sentinel-1 simulados y reales en la isla de Tenerife, España. Teniendo en cuenta los métodos que utilizan observaciones meteorológicas para mitigar APS, se ha implementado una estrategia de computación realista y precisa que utiliza datos de reanálisis atmosférico global. Con el enfoque, se considera el camino realista de LOS a lo largo del satélite y los puntos monitoreados, en lugar de convertirlos desde el retardo de la ruta cenital. En comparación con el método basado en la demora cenital, la mayor ventaja es que puede evitar errores causados por el comportamiento atmosférico anisotrópico. El método de integración preciso se valida con los datos de Sentinel-1 en tres sitios de prueba: la isla de Tenerife, España, Almería, España y la isla de Creta, Grecia. En comparación con el método cenital convencional, el método de integración realista muestra una gran mejora

Atmospheric artifacts correction for InSAR using empirical model and numerical weather prediction models

lnSAR has been proved its unprecedented ability and merits of monitoring ground deformation on large scale with centimeter to millimeter scale accuracy. However, several factors affect the reliability and accuracy of its applications. Among them, atmospheric artifacts due to spatial and temporal variations of atmosphere state often pose noise to interferograms. Therefore, atmospheric artifacts m itigalion remains one of the biggest challenges to be addressed in the In SAR community. State-of-the-art research works have revealed atmospheric artifacts can be partially compensated with empirical models, temporal-spatial filtering approach in lnSAR time series, pointwise GPS zenith path delay and numerical weather prediction models. In this thesis, firstly, we further develop a covariance weighted linear empirical model correction method. Secondly, a realistic LOS direction integration approach based on global reanalysis data is employed and comprehensively compared with the conventional method that integrates along zenith direction. Finally, the realistic integration method is applied to local WRF numerical forecast model data. l'vbreover, detailed comparisons between different global reanalysis data and local WRF model are assessed. In terms of empirical models correcting methods, many publications have studied correcting stratified tropospheric phase delay by assuming a linear model between them and topography. However, most of these studies ha\19 not considered the effect of turbulent atmospheric artefacts when adjusting the linear model to data. In this thesis, an improved technique that minimizes the influence of turbulent atmosphere in the model adjustment has been presented. In the proposed algorithm, the model is adjusted to the phase differences of pixels instead of using the unwrapped phase of each pixel. In addition, the different phase differences are weighted as a function of its APS covariance estimated from an empirical variogram to reduce in the model adjustment the impact of pixel pairs with significant turbulent atmosphere. The performance of the proposed method has been validated with both simulated and real Sentinel-1 SAR data in Tenerife island, Spain. Considering methods using meteorological observations to mitigate APS, an accurate realistic com puling strategy utilizing global atmospheric reanalysis data has been implemented. With the approach, the realistic LOS path along satellite and the monitored points is considered, rather than converting from zenith path delay. Com pared with zenith delay based method, the biggest advantage is that it can avoid errors caused by anisotropic atmospheric behaviour. The accurate integration method is validated with Sentinel-1 data in three test sites: Tenerife island, Spain, Almeria, Spain and Crete island, Greece. Compared to conventional zenith method, the realistic integration method shows great improvement. A variety of global reanalysis data are available from different weather forecasting organizations, such as ERA-Interim, ERAS, MERRA2. In this study, the realistic integration mitigation method is assessed on these different reanalysis data. The results show that these data are feasible to mitigate APS to some extent in most cases. The assessment also demonstrates that the ERAS performs the best statistically, compared to other global reanalysis data. l'vbreover, as local numerical weather forecast models have the ability to predict high spatial resolution atmospheric parameters, by using which, it has the potential to achieve APS mitigation. In this thesis, the realistic integration method is also employed on the local WRF model data in Tenerife and Almeria test s ites. However, it turns out that the WRF model performs worse than the original global reanalysis data.Las técnicas lnSAR han demostrado su capacidad sin precedentes y méritos para el monitoreo de la deformaci6n del suelo a gran escala con una precisión centimétrica o incluso milimétrica. Sin embargo, varios factores afectan la fiabilidad y precisión de sus aplicaciones. Entre ellos, los artefactos atmosféricos debidos a variaciones espaciales y temporales del estado de la atm6sfera a menudo añaden ruido a los interferogramas. Por lo tanto, la mitigación de los artefactos atmosféricos sigue siendo uno de los mayores desafíos a abordar en la comunidad lnSAR. Los trabajos de investigaci6n de vanguardia han revelado que los artefactos atmosféricos se pueden compensar parcialmente con modelos empíricos, enfoque de filtrado temporal-espacial en series temporales lnSAR, retardo puntual del camino cenital con GPS y modelos numéricos de predicción meteorológica. En esta tesis, en primer lugar, desarrollamos un método de corrección de modelo empírico lineal ponderado por covarianza. En segundo lugar, se emplea un enfoque realista de integracion de dirección LOS basado en datos de reanálisis global y se compara exhaustivamente con el método convencional que se integra a lo largo de la dirección cenital. Finalmente, el método de integraci6n realista se aplica a los datos del modelo de pronóstico numérico WRF local. Ademas, se evalúan las comparaciones detalladas entre diferentes datos de reanálisis global y el modelo WRF local. En términos de métodos de corrección con modelos empíricos, muchas publicaciones han estudiado la corrección del retraso estratificado de la fase troposférica asumiendo un modelo lineal entre ellos y la topografía. Sin embargo, la mayoría de estos estudios no han considerado el efecto de los artefactos atmosféricos turbulentos al ajustar el modelo lineal a los datos. En esta tesis, se ha presentado una técnica mejorada que minimiza la influencia de la atm6sfera turbulenta en el ajuste del modelo. En el algoritmo propuesto, el modelo se ajusta a las diferencias de fase de los pixeles en lugar de utilizar la fase sin desenrollar de cada pixel. Además, las diferentes diferencias de fase se ponderan en función de su covarianza APS estimada a partir de un variograma empírico para reducir en el ajuste del modelo el impacto de los pares de pixeles con una atm6sfera turbulenta significativa. El rendimiento del método propuesto ha sido validado con datos SAR Sentinel-1 simulados y reales en la isla de Tenerife, España. Teniendo en cuenta los métodos que utilizan observaciones meteorológicas para mitigar APS, se ha implementado una estrategia de computación realista y precisa que utiliza datos de reanálisis atmosférico global. Con el enfoque, se considera el camino realista de LOS a lo largo del satélite y los puntos monitoreados, en lugar de convertirlos desde el retardo de la ruta cenital. En comparación con el método basado en la demora cenital, la mayor ventaja es que puede evitar errores causados por el comportamiento atmosférico anisotrópico. El método de integración preciso se valida con los datos de Sentinel-1 en tres sitios de prueba: la isla de Tenerife, España, Almería, España y la isla de Creta, Grecia. En comparación con el método cenital convencional, el método de integración realista muestra una gran mejora.Postprint (published version

Spaceborne InSAR for dam stability

PhD ThesisThis study evaluates the feasibility of the use of satellite radar for dam deformation monitoring. Spaceborne Interferometric Synthetic Aperture Radar (InSAR) has long been used to monitor geohazards, including earthquakes, landslides, and volcanos. However, few studies have recently investigated its feasibility for localised deformation monitoring such as of earth dams. Here two case studies are presented of the monitoring of dams in Iraq. Mosul dam is one of the most dangerous dams in the world. Previous studies have reported that over a million human lives would be potentially at risk should dam failure occur. Therefore, investigation of its health using precise and continuous observations is crucial. This was achieved with two independent geodetic datasets from levelling and InSAR, and the results show continuous vertical displacements on the dam crest due to the dissolution of foundations. Vertical displacement rate estimates from levelling and InSAR for the period 2003-2010 are in good agreement, with a correlation of 0.93 and an RMSE of ± 1.7 mm. For the period 2014- 2017, the correlation is 0.95 and the RMSE is ± 0.9 mm. The movement of the dam was evaluated using settlement index which is not referring to critical instability of the dam. However, the spatial and temporal displacement anomalies emphasize that a careful monitoring and remedial work should continue. The continuous displacement in the dam foundation could loosen the compaction of the embankment and result in internal erosion. In a separate study, Darbandikhan dam was monitored using a global positioning system (GPS), levelling, and Sentinel-1 data to evaluate its stability after the 2017 Mw 7.3 Sarpol-e Zahab earthquake. The large gradient of the dam’s displacements on its crest hindered the estimation of co-seismic displacements using medium-resolution SAR data. However, Sentinel-1 images were sufficient to examine the dam’s stability before and after the earthquake. The results show that the dam was stable between October 2014 and November 2017, but after the earthquake continuous subsidence on the dam crest occurred between November 2017 and March 2018. For the first time the stability of the Mosul and Darbandikhan dams has been assessed using an integration of InSAR and in-situ observations. Different types of deformations were recognized, which helped in interpreting the dam’s deformation mechanismsMinistry of Higher Education (MOHE) and the State Commission of Surveys (SCOS) in Ira

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

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports