Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Thematic Layering in GIS

Thematic Layering in GIS

In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Global drainage patterns and the origins of topographic relief on Earth, Mars, and Titan.

Science (New York, N.Y.)·2017

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 15, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Edgelist phase unwrapping algorithm for time series InSAR analysis.

A Piyush Shanker¹, Howard Zebker

¹Department of Electrical Engineering, Stanford University, 350 Serra Mall, Packard 334, Stanford, California 94305, USA. shanker@stanford.edu

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|March 9, 2010

Summary

A new edgelist formulation simplifies multidimensional phase unwrapping for synthetic aperture radar interferometry. This method improves accuracy by incorporating external data and efficiently solving complex problems.

More Related Videos

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Related Experiment Videos

Last Updated: Jun 15, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Area of Science:

Geophysics
Remote Sensing
Computer Science

Background:

Phase unwrapping is crucial for coherent imaging systems like time series synthetic aperture radar interferometry (InSAR).
Existing minimum cost flow (MCF) algorithms for phase unwrapping involve complex flow calculations over closed loops.
Multidimensional data and irregularly sampled time series InSAR present significant unwrapping challenges.

Purpose of the Study:

To introduce a novel integer programming formulation for multidimensional phase unwrapping.
To develop an 'edgelist' approach that simplifies phase unwrapping by using reliable edges instead of closed loops.
To enhance the applicability of phase unwrapping to complex datasets, including time series InSAR.

Main Methods:

Developed a new integer programming formulation for phase unwrapping based on an 'edgelist' construct.
Demonstrated that the edgelist formulation exhibits total unimodularity, allowing for efficient linear programming solutions.
Incorporated external data sources (e.g., GPS) to constrain the unwrapped phase solution.

Main Results:

The edgelist method simplifies multidimensional phase unwrapping and handles both regularly and sparsely sampled data.
Applied to a persistent scatterer-InSAR dataset from the San Andreas Fault, revealing detailed creep rate variations.
The analysis showed a constant average creep rate of 22 mm/Yr from 1992-2004, with spatial variations and a temporal trend.

Conclusions:

The edgelist formulation offers a more flexible and efficient approach to multidimensional phase unwrapping.
This method is particularly well-suited for time series InSAR data, even with temporal sampling irregularities and decorrelation.
The application to the San Andreas Fault demonstrates the potential for precise geodetic measurements using this technique.