1,721,136 research outputs found
Seismic Source Quantitative Parameters Retrieval from InSAR Data and Neural Networks
No full textThe basic idea of this thesis is to exploit the capabilities of neural networks in a very new
framework: the quantitative modelling of the seismic source and the interferogram inversion for
retrieving its geometric parameters. The problem can be sum up as follows. When a moderateto-
strong earthquake occurs we can apply SAR Interferometry (InSAR) technique to compute a
differential interferogram. The latter is used to detect and measure the surface displacement
field. The earthquake has been generated by an active, seismogenic, fault having its own specific
geometry. Therefore each differential interferogram contains the information concerning the
geometry of the seismic source the earthquake comes from; its shape and size, the number of
fringes, the lobe orientation and number are the main features of the surface effects field. Two
problems have been analysed in this work. The first is the identification of the seismic source
mechanism. The second is a typical inversion exercise concerning the fault plane parameter. To
perform both exercises of the seismic fault a huge number of synthetic interferograms has been
computed. Each of them is generated by a different combination of such geometric parameters.
As far as the retrieval of the geometric parameters is concerned an artificial neural network has
been properly generated and trained to provide an inversion procedure to single out the
geometric parameters of the fault. Five among these latter, Length, Width, Dip, Strike, Depth,
have been simultaneously inverted. The result is in agreement with those results based on
different approaches. Furthermore the method seems very promising and leads to improve the
studies concerning the combined use of neural networks and InSAR technique.Tor Vergata UniversityUnpublishedope
Compression of SAR interferograms for parameter retrieval using neural networks
No full textSAR interferograms are generally affected by different types of errors. Phase noise in interferometry is introduced by the radar system, by the propagation path through the variably refractive atmosphere, by spatial decorrelation of the electromagnetic fields scattered back from the surface elements. In many applicative cases, such as DEM generation, a pixel based information is required and noise can be reduced using a multilook technique which is often applied by averaging neighboring pixels. In other cases, the pixel based information is less important with respect to the fringes distribution pattern observed over the area of interest. More specifically, in applications regarding tectonics, the retrieval problem is often focused on the estimation of the fault parameters from the InSAR differential interferogram whereas this latter is generated by computing the phase difference of two radar images, acquired before and after an earthquake, on a pixel-by-pixel basis. Elements such as the shape and periodicity of the fringes, the number of lobes and their orientation represent the information contained in the interferogram. In such a case, besides the noise mitigation, it is also important to express the relevant information after having applied to the image some feature extraction technique, in order to avoid to design inversion algorithms receiving as input the value of each single pixel. The issue can be addressed by means of a spatial sampling, but this is not certainly an optimum solution for the problem. As far as we know, no specific techniques for dimensionality reduction applied to SAR interferograms have been presented in literature. In this paper two standard image filtering approaches based on harmonic analysis and a novel one based on autoassociative neural networks (AANN) are analysed. A specific application for the estimation of tectonic parameters from SAR interferometry is also presented
15 years of SAR Interferometry
No full textSince the end of ‘80s a technique based on the use of Synthetic Aperture Radar (SAR) data from Earth-orbiting instruments has been developed. The approach, known as SAR Interferometry (InSAR) is able to provide accurate measurements of the Earth’s surface deformations due to geological and geophysical phenomena, as earthquakes, landslides, volcanic eruptions. In the timeframe of less than two decades InSAR technique spread throughout a wide range of Earth science fields. During these years a huge number of applications has been performed and a vast amount of theoretical and applicative manuscript are available in literature today. Recent improvements of InSAR lead to the development of a new approach, referred to as Advanced InSAR (A-InSAR) techniques, addressed to the monitoring of slow movements along time.
This work does not attempt to be a comprehensive review of the theory and application of InSAR and A-InSAR, but it would provide a basic overview of these techniques.Published151-1625IT. Osservazioni satellitariN/A or not JCRope
15 years of SAR Interferometry
No full textSince the end of ‘80s a technique based on the use of Synthetic Aperture Radar (SAR) data from Earth-orbiting instruments has been developed. The approach, known as SAR Interferometry (InSAR) is able to provide accurate measurements of the Earth’s surface deformations due to geological and geophysical phenomena, as earthquakes, landslides, volcanic eruptions. In the timeframe of less than two decades InSAR technique spread throughout a wide range of Earth science fields. During these years a huge number of applications has been performed and a vast amount of theoretical and applicative manuscript are available in literature today. Recent improvements of InSAR lead to the development of a new approach, referred to as Advanced InSAR (A-InSAR) techniques, addressed to the monitoring of slow movements along time.
This work does not attempt to be a comprehensive review of the theory and application of InSAR and A-InSAR, but it would provide a basic overview of these techniques.Submitted1.10. TTC - TelerilevamentoN/A or not JCRope
Automatic retrieval of tectonic parameters with neural networks and sar interferometry: An assessment with experimental data
No full textOn the ability of dual-polarimetric SAR measurements to observe lava flows under different volcanic environments
No full textIn this study, we discuss the extra-value of polarimetric information in observing the lava flow. Dual-polarimetric Synthetic Aperture Radar (SAR) measurements are processed using a polarimetric change detector that, instead of looking at the variation of the backscatter intensity between a pair of images collected before and after the event, looks at changes in the polarimetric scattering behavior. We demonstrate that the scattering changes detected by the proposed polarimetric approach well-correlate with the footprint of the lava flow provided by external sources. In addition, we also compare the performance of the polarimetric change detector with conventional single-polarization metrics showing that the former one always outperforms the incoherent single-polarization measurements. To further demonstrate the robustness of the polarimetric change detectors, we selected two test cases that refer to vulcanic eruptions calling for completely different environments. The first one, related to the Etna volcano, calls for a lava flow over a vegetation-free environment; the second one is related to the Nyiragongo volcano and calls for a lava flow in a vegetated environment. Experimental results show that the polarimetric change detectors automatically adapt to the changing environment outperforming the single-polarization detectors
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