URBAN MONITORING BASED ON SENTINEL-1 DATA USING PERMANENT SCATTERER INTERFEROMETRY AND SAR TOMOGRAPHY

A lot of research and development has been devoted to the exploitation of satellite SAR images for deformation measurement and monitoring purposes since Differential Interferometric Synthetic Apertura Radar (InSAR) was first described in 1989. In this work, we consider two main classes of advanced DInSAR techniques: Persistent Scatterer Interferometry and Tomographic SAR. Both techniques make use of multiple SAR images acquired over the same site and advanced procedures to separate the deformation component from the other phase components, such as the residual topographic component, the atmospheric component, the thermal expansion component and the phase noise. TomoSAR offers the advantage of detecting either single scatterers presenting stable proprieties over time (Persistent Scatterers) and multiple scatterers interfering within the same range-azimuth resolution cell, a significant improvement for urban areas monitoring. This paper addresses a preliminary inter-comparison of the results of both techniques, for a test site located in the metropolitan area of Barcelona (Spain), where interferometric Sentinel-1 data were analysed

An improved SAR tomographic technique using contextual information

SAR Tomography (TomoSAR) exploit multi-pass acquisitions and allow generating 3-D images, which provide an estimation of the scatterers distribution along range, azimuth and elevation. Usually the estimated elevation distribution of the scetterers in each range-azimuth pixel do not dependent on the neighboring ones (local approaches). In an urban environment, relationships among the elevations of neighboring pixels can be considerd exploiting its peculiar geometry. In this paper, the height profile is locally approximated with a planar surface and the parameters are estimated starting from the pixel values in the same position and in a “cluster” of neighboring positions. A GLRT approach is proposed to detect the presence of the scatteres. Results on simulated and real data validate the proposed approach

Enhancing properties of polyvinyl alcohol film using sorghum starch nanocrystals – A cost effective filler from natural source

Poly Vinyl Alcohol (PVA) nanocomposite films with Sorghum Starch Nanocrystals (SSN) were prepared by incorporating various concentrations of SSN in PVA.  SSN was isolated using acid hydrolysis of sorghum starch.  The morphological studies of SSN using Atomic Force Microscopy (AFM) revealed that the particle size varied from 65 to 68 nm.  The mechanical properties of PVA-SSN nanocomposite films indicated an improvement in tensile strength and percentage elongation at break when compared to that of PVA films.  This can be attributed to the stronger interaction of PVA with SSN due to hydrogen bonding.  Besides this, PVA-SSN nanocomposite films also exhibited good thermal properties.  Thus, the use of SSN (a cost effective filler from natural source) imparted superior properties to the PVA nanocomposite films, which will be beneficial for applications such as food packaging.

Mushrooms in the food culture of the <i style="mso-bidi-font-style:normal">Kaani</i> tribe of Kanyakumari district

150-153India is inhabited by over 50 million tribals belonging to 550 communities and in Tamil Nadu 36 tribal communities live, while six tribal communities are found in the Southernmost Kanyakumari district. Of the six tribes, <i style="mso-bidi-font-style: normal">Kaani tribe lives in the forests and hills of the Western Ghats. The Kaani tribe lives in consonance with Nature and their life is linked to the forest ecosystem. Though they live in settled areas, they still retain the traditional hunter-gatherer instinct, and collect their food from the forests that include mushrooms. Mushrooms contain a host of defense potentiators, which stimulate the immune system of humans. These mushrooms provide rich resources for the gene banks

Infrastructure Health Monitoring Using SAR Tomography

Static structures, such as bridges and dams, are in need of continuous structural health monitoring due to the risk of collapse or destabilization, which leads to casualties and material loss. From economical point of view, the frequent inspection of these structures is cost-ineffective and time consuming. This shortage is overcome by using remote sensing systems such as synthetic aperture radar (SAR) systems, that do not require the in situ positioning of sensors and observe the Earth on a very large spatial scale and at regular time intervals. In particular, Tomographic Synthetic Aperture Radar (TomoSAR) has proved to be an effective tool for structure health monitoring. It is a multi-dimensional imaging technique that exploits stacks of interferometric SAR images acquired with slightly different view angles and at different times. TomoSAR is able to analyze the temporal deformations and the millimeter displacements on buildings. The small movements that occur due to thermal sensitivity of the structure can be also captured, so that the coefficient of thermal expansion, which is a fundamental parameter in health structure monitoring, can be computed. The widespread deterioration and some recent collapses of infrastructures have highlighted the importance of developing early warning monitoring strategies, devoted to identify structural problems before they become critical. A problem to be considered is that effective monitoring techniques have to rely on approaches capable of identifying few representative points, which describe well the dynamical behavior of the structure. A TomoSAR technique recently developed, based on a multi-step binary hypothesis GLRT test, has shown a good capability of selecting reliable control points on the structure for estimating its deformations. In order to demonstrate the potentialities of this technique for structure health monitoring, two tall structures will be considered. The results obtained using different SAR sensors, such as COSMO-SkyMed, Sentinel-1A and TerraSAR-X, are presented

TOMOSAR APPLICATION FOR EARLY WARNING IN INFRASTRUCTURE HEALTH MONITORING

Earth observation using spaceborne sensors with short revisiting time has forgone the limits of conventional assessment methods. In this paper, we investigate the use of SAR Tomography (TomoSAR) to monitor infrastructures, using Morandi Bridge in Genoa, Italy, as a case study. Morandi Bridge suffered a partial collapse due to a structural failure on August 14, 2018. The main issues to be considered for using TomoSAR as a means of an early warning system in structural health monitoring are discussed, highlighting the differences respect to monitoring applications dealing with subsidence and deformation of extended areas.A set of 70 Sentinel-1A images of the bridge over a period of three years prior to the bridge collapse are analyzed using 5D TomoSAR based on Sup-GLRT scatterers detection technique. Preliminary results indicate that possible temporal and thermal deformations of the static structure can be estimated using the method considered

Multiple scatterers detection based on signal correlation eploitation in urban SAR tomography

This paper addresses the problem of SAR Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers in presence of partially correlated Gaussian clutter. TomoSAR is a multidimensional imaging technique that has proven its ability in localizing the scatterers, reconstructing the elevation profile of the structures on the ground (3D reconstruction) and estimating the temporal deformations and thermal dilations of the scene (5D reconstruction). In the literature statistical based TomoSAR reconstruction refers to a signal model where in each range-azimuth resolution cell one or more scatterers are interfering in presence of noise and clutter signals, modeled as zero-mean complex circular white Gaussian random vectors. In this paper, we propose to extend a generalized likelihood ratio test (GLRT) detector, proposed by the authors and denoted Fast-Sup-GLRT, to a different signal model, where a correlated clutter model is considered. Results on TerraSAR-X real data are presented