Scale Object Selection (SOS) through a hierarchical segmentation by a multi-spectral per-pixel classification

International audienceIn high resolution multispectral optical data, the spatial detail of the images are generally smaller than the dimensions of objects, and often the spectral signature of pixels is not directly representative of classes we are interested in. Thus, taking into account the relations between groups of pixels becomes increasingly important, making object­oriented approaches preferable. In this work several scales of detail within an image are considered through a hierarchical segmentation approach, while the spectral information content of each pixel is accounted for by a per­pixel classification. The selection of the most suitable spatial scale for each class is obtained by merging the hierarchical segmentation and the per­pixel classification through the Scale Object Selection (SOS) algorithm. The SOS algorithm starts processing data from the highest level of the hierarchical segmentation, which has the least amount of spatial detail, down to the last segmentation map. At each segmentation level, objects are assigned to a specific class whenever the percentage of pixels belonging to the latter, according to a pixel­based procedure, exceeds a predefined threshold, thereby automatically selecting the most appropriate spatial scale for the classification of each object. We apply our method to multispectral, panchromatic and pan­sharpened QuickBird images

3D displacement field retrieved by integrating Sentinel-1 InSAR and GPS data: the 2014 South Napa earthquake

In this study the integration of Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) and GPS (Global Positioning System) data was performed to estimate the three components of the ground deformation field due to the Mw 6.0 earthquake occurred on August 24th, 2014, in the Napa Valley, California, USA. The SAR data were acquired by the Sentinel-1 satellite on August 7th and 31st respectively. In addition, the GPS observations acquired during the whole month of August were analyzed. These data were obtained from the Bay Area Regional Deformation Network, the UNAVCO and the Crustal Dynamics Data Information System online archives. The data integration was realized by using a Bayesian statistical approach searching for the optimal estimation of the three deformation components. The experimental results show large displacements caused by the earthquake characterized by a predominantly NW-SE strike-slip fault mechanism.The research has been supported by the “Marco Polo” project by the University of Bologna (UNIBO), the Spanish Ministry of Economy and Competitiveness research project ESP2013-47780-557 C2-1-R and the EU 7th FP MED-SUV project (contract 308665).Peer reviewe

Combined Ground Deformation Study Of Broader Area Of Patras Gulf (W. Greece) Using PSI-WAP, DGPS And Seismicity Analyses

Long-term ground deformation monitoring using the Persistent Scatterer Interferometry Wide Area Product (PSI-WAP) technique for the period 1992-2003, combined with Differential GPS measurements and seismicity analysis has provided useful information about the tectonic motions of the tectonically complex area of Patras Gulf (Western Greece), and lead to new insights on the geotectonic regime of this region. Descending ERS radar images were used to compile the PSI-WAP product that has been calibrated using the absolute velocity field of available GPS stations in the area. It has been found that the deformation of the southern part of Patras Gulf near the coastline has been characterized by considerable subsidence (>-5mm/yr), where unconsolidated sediments usually prevail, compared to the northern part of the gulf. Significant subsidence has also been identified in areas along the down-throw side of possible faults, as well as areas where extensive ground water pumping has occurred for irrigation. These results correlate well with local GPS and seismicity data

Decomposing DInSAR Time-Series into 3-D in Combination with GPS in the Case of Low Strain Rates: An Application to the Hyblean Plateau, Sicily, Italy

Differential Interferometric SAR (DInSAR) time-series techniques can be used to derive surface displacement rates with accuracies of 1 mm/year, by measuring the one-dimensional distance change between a satellite and the surface over time. However, the slanted direction of the measurements complicates interpretation of the signal, especially in regions that are subject to multiple deformation processes. The Simultaneous and Integrated Strain Tensor Estimation from Geodetic and Satellite Deformation Measurements (SISTEM) algorithm enables decomposition into a three-dimensional velocity field through joint inversion with GNSS measurements, but has never been applied to interseismic deformation where strain rates are low. Here, we apply SISTEM for the first time to detect tectonic deformation on the Hyblean Foreland Plateau in South-East Sicily. In order to increase the signal-to-noise ratio of the DInSAR data beforehand, we reduce atmospheric InSAR noise using a weather model and combine it with a multi-directional spatial filtering technique. The resultant three-dimensional velocity field allows identification of anthropogenic, as well as tectonic deformation, with sub-centimeter accuracies in areas of sufficient GPS coverage. Our enhanced method allows for a more detailed view of ongoing deformation processes as compared to the single use of either GNSS or DInSAR only and thus is suited to improve assessments of regional seismic hazard

earthquake damage mapping by using remotely sensed data the haiti case study

This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west–south–west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback–Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale

Ground Deformation Imagery of the May Sichuan Earthquake

The magnitude Mw = 7.8 earthquake that struck China's Sichuan region on 12 May 2008 (Figure 1a) has been imaged by the Italian Space Agency's (ASI) Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO)‐SkyMed radar Earth observation satellites. Five images were available—two preseismic spotlight mode images and three strip‐map mode images, two of which are preseismic and one of which is postseismic. We used two strip‐map images (acquired 1 month prior to and 3 days after the earthquake) to generate the first ever X‐band (i.e., microwave frequency domain, corresponding to about 3‐ centi meter wavelength) coseismic interferogram, which clearly shows part of the strong ground deformation caused by the fault dislocation. We also performed a change detection analysis of the same data that highlighted several changes in the radar response, presumably due to strong seismic damage, as far as 80 kilo meters away from the epicenter

The Interferometric Use of Radar Sensors for the Urban Monitoring of Structural Vibrations and Surface Displacements

In this paper, we propose a combined use of real aperture radar (RAR) and synthetic aperture radar (SAR) sensors, within an interferometric processing chain, to provide a new methodology for monitoring urban environment and historical buildings at different temporal and spatial scales. In particular, ground-based RAR measurements are performed to estimate the vibration displacements and the natural oscillation frequencies of structures, with the aim of supporting the understanding of the building dynamic response. These measurements are then juxtaposed with ground-based and space-borne SAR data to monitor surface deformation phenomena, and hence, point out potential risks within an urban environment. In this framework, differential interferometric SAR algorithms are implemented to generate short-term (monthly) surface displacement and long-term (annual) mean surface displacement velocity maps at local (hundreds m2) and regional (tens km2) scale, respectively. The proposed methodology, developed among the activities carried out within the national project Programma Operativo Nazionale MASSIMO (Monitoraggio in Area Sismica di SIstemi MOnumentali), is tested and discussed for the ancient structure of Saint Augustine compound, located in the historical center of Cosenza (Italy) and representing a typical example of the Italian Cultural Heritage

USE OF NEURAL NETWORKS AND SAR INTERFEROMETRY FOR THE AUTOMATIC RETRIEVAL OF TECTONIC PARAMETERS

ABSTRACT From its first application in 1992 to detect the displacement field originated from the Landers earthquake In the recent years InSAR capabilities, together with classic seismological and geophysical data such as strong motion records and GPS, have also been used by geophysicists for the assessment of normal fault models Neural networks have already been recognized as being a powerful tool for inversion procedure in remote sensing applications In this study we propose an innovative approach for the seismic source classification and the fault parameter quantitative retrieval. The originality of such an approach consists in exploiting at the same time the capabilities of neural networks and of InSAR measurements in the described context. The network is trained by using a data set generated by the RNGCHN software and then tested on real measured data. The input of the net consists of a set of features calculated from the interferometric image while the output vector contains the parameters characterizing the fault. Two problems have been analysed. The first one is the identification of the seismic source mechanism. The second one addresses the fault plane parameters estimation. The paper illustrates such a methodology and its validation on a set of experimental data. The experimental set up was composed by three case studies covering different types of faults: normal, strike slip, reverse