Coordinates in the 3D space of elements in a SAR image can be obtained by the combination of along-track, slant-range and interferometric fringe measurements. In order to evaluate the elevation of a pixel with respect to a slant-range reference plane, its absolute interferometric phase is required and this is typically derived unwrapping a 2D interferometric fringe pattern. Phase inconsistencies (residues) in SAR interferograms due to noise and topography determine unwrapping errors which appear as discontinuities in the computed absolute phase field. Phase aliasing arising from rapid phase variations from topography generates two dimensional unwrapping inconsistencies characterized by phase patterns statistically different from those induced by noise. In this paper, the spatial configurations of the phase field around residues is utilized in the phase unwrapping procedure. The feasibility of a neural network approach for classifying residual complex geometric phase patterns requiring different corrective measures is also presented. In addition, a method based on pseudo-differential interferometry to resolve residual inconsistencies as noise- or topography-generated is explored.

Phase unwrapping techniques for INSAR

Pasquariello G;Refice A;Veneziani N
1996

Abstract

Coordinates in the 3D space of elements in a SAR image can be obtained by the combination of along-track, slant-range and interferometric fringe measurements. In order to evaluate the elevation of a pixel with respect to a slant-range reference plane, its absolute interferometric phase is required and this is typically derived unwrapping a 2D interferometric fringe pattern. Phase inconsistencies (residues) in SAR interferograms due to noise and topography determine unwrapping errors which appear as discontinuities in the computed absolute phase field. Phase aliasing arising from rapid phase variations from topography generates two dimensional unwrapping inconsistencies characterized by phase patterns statistically different from those induced by noise. In this paper, the spatial configurations of the phase field around residues is utilized in the phase unwrapping procedure. The feasibility of a neural network approach for classifying residual complex geometric phase patterns requiring different corrective measures is also presented. In addition, a method based on pseudo-differential interferometry to resolve residual inconsistencies as noise- or topography-generated is explored.
1996
Istituto di Studi sui Sistemi Intelligenti per l'Automazione - ISSIA - Sede Bari
0-8194-2362-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/5585
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