Abstract
In this article, a new technique for features extraction from SAR interferograms is presented. The technique combines the properties of auto-associative neural networks with those of more traditional approaches such as discrete Fourier transform or discrete wavelet transform. The feature extraction is chained to another neural module performing the estimation of the fault parameters characterizing a seismic event. The whole procedure has been validated with the experimental data acquired for the analysis of the dramatic L’Aquila earthquake which occurred in Italy in 2009. The results show the effectiveness of the approach either in terms of dimensionality reduction or in terms retrieval capabilities.
Highlights
Cross-track radar interferometry is a processing technique of synthetic aperture radar (SAR) data based on the generation of an interferogram using two complex images of the same area acquired with slightly different look angles
A preliminary analysis has been performed considering a set of 120 synthetic interferograms to identify the average number of coefficients that retain the 80% of the total cumulative energy (CE), for the discrete Fourier transform (DFT) and discrete wavelet transform (DWT), respectively
In this study, we addressed the problem of feature extraction from SAR interferograms in the particular framework of the analysis of tectonic events
Summary
Cross-track radar interferometry is a processing technique of synthetic aperture radar (SAR) data based on the generation of an interferogram using two complex images of the same area acquired with slightly different look angles (for a more detailed treatment refer to Bürgmann et al [1]). A network with fewer weights may be faster to train All these benefits make the reduction in the dimension of the input data a normal procedure when designing NN, even for a relatively low dimensional input space. Starting from these motivations in this article we present a new technique to extract the essential features contained in a SAR interferogram image. The seismic parameter estimation is considered as the field of application, the same technique can be used for other scenarios where the fringes spatial distribution is the critical information of the image and an approach for dimensionality reduction is required
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