We present an efficient space-time phase unwrapping (PhU) algorithm allowing us to analyze sequences of multitemporal full resolution differential Synthetic Aperture Radar (SAR) interferograms for the generation of deformation time-series. The core of the proposed technique is represented by the extended minimum cost flow (EMCF) PhU algorithm. Our method is based on a joint analysis of the spatial and temporal relationships among a set of properly selected multitemporal differential interferograms. More specifically, the key idea is to split the complex MCF network problem into that of simpler sub-networks. Accordingly, we start by identifying and solving a primary network that involves a proper selection of coherent pixels of the computed interferograms, representing the backbone structure of the overall network. Subsequently, this result is applied for constraining the solution of the sub-networks connected to the primary one, involving the entire set of analyzed pixels. This task is achieved by solving a constrained optimization problem based on the computation of a Constrained Delaunay Triangulation in the Azimuth/Range domain. The experimental results, achieved by applying the proposed approach to a dataset consisting of European Remote Sensing (ERS) SAR data acquired, from June 1992 to August 2007 over the Napoli (Italy) bay area, confirm the effectiveness of the proposed PhU approach.

New improvements of the extended minimum cost flow phase unwrapping for processing multitemporal full resolution interferograms

MANUNTA, MICHELE;
2011-01-01

Abstract

We present an efficient space-time phase unwrapping (PhU) algorithm allowing us to analyze sequences of multitemporal full resolution differential Synthetic Aperture Radar (SAR) interferograms for the generation of deformation time-series. The core of the proposed technique is represented by the extended minimum cost flow (EMCF) PhU algorithm. Our method is based on a joint analysis of the spatial and temporal relationships among a set of properly selected multitemporal differential interferograms. More specifically, the key idea is to split the complex MCF network problem into that of simpler sub-networks. Accordingly, we start by identifying and solving a primary network that involves a proper selection of coherent pixels of the computed interferograms, representing the backbone structure of the overall network. Subsequently, this result is applied for constraining the solution of the sub-networks connected to the primary one, involving the entire set of analyzed pixels. This task is achieved by solving a constrained optimization problem based on the computation of a Constrained Delaunay Triangulation in the Azimuth/Range domain. The experimental results, achieved by applying the proposed approach to a dataset consisting of European Remote Sensing (ERS) SAR data acquired, from June 1992 to August 2007 over the Napoli (Italy) bay area, confirm the effectiveness of the proposed PhU approach.
2011
978-145771005-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/69987
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