Polarimetric Interferometric SAR Change Detection Discrimination

West, Roger Derek; Riley, Robert M.

doi:10.1109/TGRS.2018.2879787

Title: Polarimetric Interferometric SAR Change Detection Discrimination

Abstract

A coherent change detection (CCD) image, computed from a geometrically matched, temporally separated pair of complex-valued synthetic aperture radar (SAR) image sets, conveys the pixel-level equivalence between the two observations. Low-coherence values in a CCD image are typically due to either some physical change in the corresponding pixels or a low signal-to-noise observation. A CCD image does not directly convey the nature of the change that occurred to cause low coherence. In this paper, we introduce a mathematical framework for discriminating between different types of change within a CCD image. We utilize the extra degrees of freedom and information from polarimetric interferometric SAR (PolInSAR) data and PolInSAR processing techniques to define a 29-dimensional feature vector that contains information capable of discriminating between different types of change in a scene. We also propose two change-type discrimination functions that can be trained with feature vector training data and demonstrate change-type discrimination on an example image set for three different types of change. In conclusion, we also describe and characterize the performance of the two proposed change-type discrimination functions by way of receiver operating characteristic curves, confusion matrices, and pass matrices.

Authors:

^[1];

^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Publication Date:: Fri Dec 14 00:00:00 EST 2018

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1492857

Report Number(s):: SAND-2018-12632J
Journal ID: ISSN 0196-2892; 668710

Grant/Contract Number:: AC04-94AL85000

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Geoscience and Remote Sensing

Additional Journal Information:: Journal Volume: 57; Journal Issue: 6; Journal ID: ISSN 0196-2892

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 58 GEOSCIENCES; Coherent change detection (CCD); feature vector; H/A/α decomposition; H/A/α filter; optimum coherence (OC); polarimetric interferometric synthetic aperture radar (PolInSAR); probabilistic feature fusion (PFF) model

Citation Formats


                    West, Roger Derek, and Riley, Robert M. Polarimetric Interferometric SAR Change Detection Discrimination.  United States: N. p., 2018. 
Web.  doi:10.1109/TGRS.2018.2879787.

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                    West, Roger Derek, & Riley, Robert M. Polarimetric Interferometric SAR Change Detection Discrimination.  United States.  https://doi.org/10.1109/TGRS.2018.2879787

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                    West, Roger Derek, and Riley, Robert M. Fri .  
"Polarimetric Interferometric SAR Change Detection Discrimination".  United States.  https://doi.org/10.1109/TGRS.2018.2879787.  https://www.osti.gov/servlets/purl/1492857.

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@article{osti_1492857,

  title        = {Polarimetric Interferometric SAR Change Detection Discrimination},

  author       = {West, Roger Derek and Riley, Robert M.},

  abstractNote = {A coherent change detection (CCD) image, computed from a geometrically matched, temporally separated pair of complex-valued synthetic aperture radar (SAR) image sets, conveys the pixel-level equivalence between the two observations. Low-coherence values in a CCD image are typically due to either some physical change in the corresponding pixels or a low signal-to-noise observation. A CCD image does not directly convey the nature of the change that occurred to cause low coherence. In this paper, we introduce a mathematical framework for discriminating between different types of change within a CCD image. We utilize the extra degrees of freedom and information from polarimetric interferometric SAR (PolInSAR) data and PolInSAR processing techniques to define a 29-dimensional feature vector that contains information capable of discriminating between different types of change in a scene. We also propose two change-type discrimination functions that can be trained with feature vector training data and demonstrate change-type discrimination on an example image set for three different types of change. In conclusion, we also describe and characterize the performance of the two proposed change-type discrimination functions by way of receiver operating characteristic curves, confusion matrices, and pass matrices.},

  doi          = {10.1109/TGRS.2018.2879787},

  journal      = {IEEE Transactions on Geoscience and Remote Sensing},

  number       = 6,

  volume       = 57,

  place        = {United States},

  year         = {Fri Dec 14 00:00:00 EST 2018},

  month        = {Fri Dec 14 00:00:00 EST 2018}

}

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Works referencing / citing this record:

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Citation Formats

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A PolSAR Change Detection Index Based on Neighborhood Information for Flood Mapping
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