Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points – A Review

Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram

InSAR as a tool for monitoring hydropower projects: A review

This paper provides a review of using Interferometric Synthetic Aperture Radar (InSAR), a microwave remote sensing technique, for deformation monitoring of hydroelectric power projects, a critical infrastructure that requires consistent and reliable monitoring. Almost all major dams around the world were built for the generation of hydropower. InSAR can enhance dam safety by providing timely settlement measurements at high spatial-resolution. This paper provides a holistic view of different InSAR deformation monitoring techniques such as Differential Synthetic Aperture Radar Interferometry (DInSAR), Ground-Based Synthetic Aperture Radar (GBInSAR), Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), Multi-Temporal Interferometric Synthetic Aperture Radar (MTInSAR), Quasi-Persistent Scatterer Interferometric Synthetic Aperture Radar (QPSInSAR) and Small BAseline Subset (SBAS). PSInSAR, GBInSAR, MTInSAR, and DInSAR techniques were quite commonly used for deformation studies. These studies demonstrate the advantage of InSAR-based techniques over other conventional methods, which are laborious, costly, and sometimes unachievable. InSAR technology is also favoured for its capability to provide monitoring data at all times of day or night, in all-weather conditions, and particularly for wide areas with mm-scale precision. However, the method also has some disadvantages, such as the maximum deformation rate that can be monitored, and the location for monitoring cannot be dictated. Through this review, we aim to popularize InSAR technology to monitor the deformation of dams, which can also be used as an early warning method to prevent any unprecedented catastrophe. This study also discusses some case studies from southern India to demonstrate the capabilities of InSAR to indirectly monitor dam health

Satellite SAR interferometry for monitoring dam deformation in Portugal

The paper offers three examples of satellite SAR interferometry (InSAR) application for monitoring dam deformations: Paradela, Raiva and Alto Ceira, all of them in Portugal. Dam deformations were estimated using several sets of ERS and Envisat C-band SAR data by PS-InSAR method that offers accuracy of a millimeter per year at monitoring man-made tructures. The results show potential of InSAR but also summarize limits of C-band InSAR in these particular cases and can be handful to recognize applicability of new Sentinel-1 data (since 2014) for continuous monitoring of dam deformations. While Alto Ceira dam lies in SAR radar shadow and was represented by only one observable point, and the movement detected (in satellite line-of-sight direction) appears to fit with geodetical measurements. Raiva and Paradela dams were represented by sufficient number of points feasible for PS-InSAR processing. Deformations at slope near to Raiva dam and slow linear movements of the center of Paradela dam were detected

Monitoring land subsidence of airport using InSAR time-series techniques with atmospheric and orbital error corrections

Land subsidence is one of the common geological hazards worldwide and mostly caused by human activities including the construction of massive infrastructures. Large infrastructure such as airport is susceptible to land subsidence due to several factors. Therefore, monitoring of the land subsidence at airport is crucial in order to prevent undesirable loss of property and life. Remote sensing technique, especially Interferometric Synthetic Aperture Radar (InSAR) has been successfully applied to measure the surface deformation over the past few decades although atmospheric artefact and orbital errors are still a concerning issue in this measurement technique. Multi-temporal InSAR, an extension of InSAR technique, uses large sets of SAR scenes to investigate the temporal evolution of surface deformation and mitigate errors found in a single interferogram. This study investigates the long-term land subsidence of the Kuala Lumpur International Airport (KLIA), Malaysia and Singapore Changi Airport (SCA), Singapore by using two multi-temporal InSAR techniques like Small Baseline Subset (SBAS) and Multiscale InSAR Time Series (MInTS). General InSAR processing was conducted to generate interferogram using ALOS PALSAR data from 2007 until 2011. Atmospheric and orbital corrections were carried out for all interferograms using weather model, namely European Centre for Medium Range Weather Forecasting (ECMWF) and Network De-Ramping technique respectively before estimating the time series land subsidence. The results show variation of subsidence with respect to corrections (atmospheric and orbital) as well as difference between multi-temporal InSAR techniques (SBAS and MInTS) used. After applying both corrections, a subsidence ranging from 2 to 17 mm/yr was found at all the selected areas at the KLIA. Meanwhile, for SCA, a subsidence of about less than 10 mm/yr was found. Furthermore, a comparison between two techniques (SBAS and MInTS) show a difference rate of subsidence of about less than 1 mm/yr for both study area. SBAS technique shows more linear result as compared to the MInTS technique which shows slightly scattering pattern but both techniques show a similar trend of surface deformation in both study sites. No drastic deformation was observed in these two study sites and slight deformation was detected which about less than 20mm/yr for both study areas probably occurred due to several reasons including conversion of the land use from agricultural land, land reclamation process and also poor construction. This study proved that InSAR time series surface deformation measurement techniques are useful as well as capable to monitor deformation of large infrastructure such as airport and as an alternative to costly conventional ground measurement for infrastructure monitoring

Monitorización de infraestructuras críticas expuestas a riesgos naturales y antrópicos mediante interferometría radar de satélite

[EN] Synthetic Aperture Radar Interferometry (InSAR) is a remote sensing technique very effective for the measure of smalldisplacements of the Earth’s surface over large areas at a very low cost as compared with conventional geodetictechniques. Advanced InSAR time series algorithms for monitoring and investigating surface displacement on Earth arebased on conventional radar interferometry. These techniques allow us to measure deformation with uncertainties of 1mm/year, interpreting time series of interferometric phases at coherent point scatterers (PS) without the need for humanor special equipment presence on the site. By applying InSAR processing techniques to a series of radar images over thesame region, it is possible to detect line-of-sight (LOS) displacements of infrastructures on the ground and therefore identifyabnormal or excessive movement indicating potential problems requiring detailed ground investigation. A major advantageof this technology is that a single radar image can cover a major area of up to 100 km by 100 km or more as, for example,Sentinel-1 C-band satellites data cover a 250 km wide swath. Therefore, all engineering infrastructures in the area, suchas dams, dikes, bridges, ports, etc. subject to terrain deformation by volcanos, landslides, subsidence due to groundwater,gas, or oil withdrawal could be monitored, reducing operating costs effectively. In this sense, the free and open accessCopernicus Sentinel-1 data with currently up to 6-days revisit time open new opportunities for a near real-time landmonitoring. In addition, the new generation of high-resolution radar imagery acquired by SAR sensors such as TerraSARX,COSMO-SkyMed, and PAZ, and the development of multi-interferogram techniques has enhanced our capabilities inrecent years in using InSAR as deformation monitoring tool. In this paper, we address the applicability of using spaceborneSAR sensors for monitoring infrastructures in geomatics engineering and present several cases studies carried out by ourgroup related to anthropogenic and natural hazards, as well as monitoring of critical infrastructures.[ES] La interferometría radar de apertura sintética (InSAR) es una técnica de teledetección muy eficaz para medir pequeños desplazamientos de la superficie terrestre en grandes áreas a un coste muy pequeño en comparación con las técnicas geodésicas convencionales. Los algoritmos avanzados de series temporales InSAR para monitorizar e investigar el desplazamiento de la superficie terrestre se basan en la interferometría radar convencional. Estas técnicas nos permiten medir la deformación con incertidumbres de un milímetro por año, interpretando series temporales de fases interferométricas en retrodispersores puntuales coherentes (PS) sin necesidad de presencia humana o de equipos especiales en el sitio. Al aplicar técnicas de procesamiento InSAR a una serie de imágenes radar de la misma región, es posible detectar desplazamientos de infraestructuras proyectados en la línea de vista del satélite (line-of-sight o LOS) y, por lo tanto, identificar movimientos anormales o excesivos que indiquen problemas potenciales que requieran una investigación detallada del terreno. Una de las principales ventajas de esta tecnología es que una sola imagen radar puede cubrir un área importante de hasta 100 km por 100 km o más, ya que, por ejemplo, los datos de los satélites de banda C Sentinel-1 cubren una franja de 250 km de ancho. Por lo tanto, todas las infraestructuras civiles de la zona, como presas, diques, puentes, puertos, etc., sujetas a deformaciones del terreno por actividad volcánica, deslizamientos de tierra, hundimientos por extracción de agua subterránea, gas o petróleo, podrían ser monitorizados, reduciendo los costes operativos de manera efectiva. En este sentido, los datos Sentinel-1 de Copernicus, de acceso abierto, con hasta 6 días de tiempo de revisión actual abren nuevas oportunidades para una monitorización terrestre casi en tiempo real. Además, la nueva generación de imágenes radar de alta resolución adquiridas por sensores SAR como TerraSAR-X, COSMOSkyMed y PAZ, y el desarrollo de técnicas multi-interferograma ha mejorado nuestras capacidades en los últimos años en el uso del InSAR como herramienta para el control de deformaciones. En este trabajo se aborda la aplicabilidad del uso de sensores SAR espaciales para la monitorización de infraestructuras civiles en ingeniería geomática y presentamos varios casos de estudio realizados por nuestro grupo relacionados con riesgos naturales y antrópicos, así como de monitorización de infraestructura crítica.ERS-1/2 and Envisat datasets were provided by the European Space Agency (ESA). Sentinel-1A/B data were freely provided by ESA through Copernicus Programme. Data have been processed by DORIS (TUDelft), StaMPS (Andy Hooper), SARPROZ (Copyright (c) 2009-2020 Daniele Perissin), and SNAP (ESA). The satellite orbits are from TUDelft and ESA, as well as from the ESA Quality Control Group of Sentinel-1. Research was supported by [ESA Research and Service Support] for providing hardware resources employed in this work; [Spanish Ministry of Economy, Industry and Competitiveness] under ReMoDams project ESP2017-89344-R (AEI/FEDER, UE); [University of Jaén (Spain)] under PAIUJA-2021/2022 and CEACTEMA; [Junta de Andalucía (Spain)] under RNM-282 research group; [ERDF through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme] within project «POCI-01-0145-FEDER006961»; [National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology)] as part of project UID/EEA/50014/2013; [The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II)] under project «IT4Innovations excellence in science - LQ1602» (Czech Republic); and [Slovak Grant Agency VEGA] under projects No. 2/0100/20Ruiz-Armenteros, A.; Delgado-Blasco, J.; Bakon, M.; Lazecky, M.; Marchamalo-Sacristán, M.; Lamas-Fernández, F.; Ruiz-Constán, A.... (2021). Monitoring critical infrastructure exposed to anthropogenic and natural hazards using satellite radar interferometry. En Proceedings 3rd Congress in Geomatics Engineering. Editorial Universitat Politècnica de València. 137-146. https://doi.org/10.4995/CiGeo2021.2021.12736OCS13714

SAR Tomography via Nonlinear Blind Scatterer Separation

Layover separation has been fundamental to many synthetic aperture radar applications, such as building reconstruction and biomass estimation. Retrieving the scattering profile along the mixed dimension (elevation) is typically solved by inversion of the SAR imaging model, a process known as SAR tomography. This paper proposes a nonlinear blind scatterer separation method to retrieve the phase centers of the layovered scatterers, avoiding the computationally expensive tomographic inversion. We demonstrate that conventional linear separation methods, e.g., principle component analysis (PCA), can only partially separate the scatterers under good conditions. These methods produce systematic phase bias in the retrieved scatterers due to the nonorthogonality of the scatterers' steering vectors, especially when the intensities of the sources are similar or the number of images is low. The proposed method artificially increases the dimensionality of the data using kernel PCA, hence mitigating the aforementioned limitations. In the processing, the proposed method sequentially deflates the covariance matrix using the estimate of the brightest scatterer from kernel PCA. Simulations demonstrate the superior performance of the proposed method over conventional PCA-based methods in various respects. Experiments using TerraSAR-X data show an improvement in height reconstruction accuracy by a factor of one to three, depending on the used number of looks.Comment: This work has been accepted by IEEE TGRS for publicatio

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments

Method for landslides detection with semi-automatic procedures: The case in the zone center-east of Cauca department, Colombia

Landslides are a common natural hazard that causes human casualties, but also infrastructure damage and land-use degradation. Therefore, a quantitative assessment of their presence is required by means of detecting and recognizing the potentially unstable areas. This research aims to develop a method supported on semiautomatic methods to detect potential mass movements at a regional scale. Five techniques were studied: Morphometry, SAR interferometry (InSAR), Persistent Scatterer InSAR (PS-InSAR), SAR polarimetry (PolSAR) and NDVI composites of Landsat 5, Landsat 7, and Landsat 8. The case study was chosen within the mid-eastern area of the Cauca state, which is characterised by its mountainous terrain and the presence of slope instabilities, officially registered in the CGS-SIMMA landslide inventory. This inventory revealed that the type `slide' occurred with 77.4% from the entire registries, `fall' with 16.5%, followed by `creeps' with 3%, flows with 2.6%, and `lateral spread' with 0.43%. As a result, we obtained the morphometric variables: slope, CONVI, TWI, landform, which were highly associated with landslides. The effect of a DEM in the processing flow of the InSAR method was similar for the InSAR coherence variable using the DEMs ASTER, PALSAR RTC, Topo-map, and SRTM. Then, a multiInSAR analysis gave displacement velocities in the LOS direction between -10 and 10 mm/year. With the dual-PolSAR analysis (Sentinel-1), VH and VV C-band polarised radar energy emitted median values of backscatters, for landslides, about of -14.5 dB for VH polarisation and -8.5 dB for VV polarisation. Also, L-band fully polarimetric NASA-UAVSAR data allowed to nd the mechanism of dispersion of CGS landslide inventory: 39% for surface scattering, 46.4% for volume dispersion, and 14.6% for double-bounce scattering. The optical remote sensing provided NDVI composites derived from Landsat series between 2012 and 2016, showing that NDVI values between 0.40 and 0.70 had a high correlation to landslides. In summary, we found the highest categories related to landslides by Weight of Evidence method (WofE) for each spaceborne technique applied. Finally, these results were merged to generate the landslide detection model by using the supervised machine learning method of Random Forest. By taking training and test samples, the precision of the detection model was of about 70% for the rotational and translational types.Los deslizamientos son una amenaza natural que causa pérdidas humanas, daños a la infraestructura y degradación del suelo. Una evaluación cuantitativa de su presencia se requiere mediante la detección y el reconocimiento de potenciales áreas inestables. Esta investigación tuvo como alcance desarrollar un método soportado en métodos semi-automáticos para detectar potenciales movimientos en masa a escala regional. Cinco técnicas fueron estudiadas: Morfometría, Interferometría radar, Interferometría con Persistent Scatterers, Polarimetría radar y composiciones del NDVI con los satélites Landsat 5, Landsat 7 y Landsat 8. El caso de estudio se seleccionó dentro de la región intermedia al este del departamento del Cauca, la cual se caracteriza por terreno montañoso y la presencia de inestabilidades de la pendiente oficialmente registrados en el servicio SIMMA del Servicio Geológico Colombiano. Este inventario reveló que el tipo de movimiento deslizamiento ocurrió con una frecuencia relativa de 77.4%, caidos con el 16.5% de los casos y reptaciones con 3%, flujos con 2.6% y propagación lateral con 0.43%. Como resultado, se obtuvo las variables morfométricas: pendiente, convergencia, índice topográfico de humedad y forma del terreno altamente asociados con los deslizamientos. El efecto de un DEM en el procesamiento del método InSAR fue similar para la variable coherencia usando los DEMs: ASTER, PAlSAR RTC, Topo-map y SRTM. Un análisis Multi-InSAR estimó velocidades de desplazamiento en dirección de vista del radar entre -10 y 10 mm/año. El análisis de polarimetría dual del Sentinel-1 arrojó valores de retrodispersión promedio de -14.5 dB en la banda VH y -8.5dB en la banda VV. Las cuatro polarimetrías del sensor aéreo UAVSAR permitió caracterizar el mecanismo de dispersión del Inventario de Deslizamiento así: 39% en el mecanismo de superficie, 46.4% en el mecanismo de volumen y 14.6% en el mecanismo de doble rebote. La información generada en el rango óptico permitió obtener composiciones de NDVI derivados de la plataforma Landsat entre los años 2012 y 2016, mostrando que el rango entre 0.4 y 0.7 tuvieron una alta asociación con los deslizamientos. En esta investigación se determinaron las categorías de las variables de Teledetección más altamente relacionadas con los movimientos en masa mediante el método de Pesos de Evidencias (WofE). Finalmente, estos resultados se fusionaron para generar el modelo de detección de deslizamientos usando el método supervisado de aprendizaje de máquina Random Forest. Tomando muestras aleatorias para entrenar y validar el modelo en una proporción 70:30, el modelo de detección, especialmente los movimientos de tipo rotacional y traslacional fueron clasificados con una tasa general de éxito del 70%.Ministerio de CienciasConvocatoria 647 de 2014Research line: Geotechnics and Geoenvironmental HazardDoctorad