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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:
ORCiD logo [1]; ORCiD logo [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
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:
Journal Article: 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.
West, Roger Derek, & Riley, Robert M. Polarimetric Interferometric SAR Change Detection Discrimination. United States. doi:10.1109/TGRS.2018.2879787.
West, Roger Derek, and Riley, Robert M. Fri . "Polarimetric Interferometric SAR Change Detection Discrimination". United States. doi:10.1109/TGRS.2018.2879787. https://www.osti.gov/servlets/purl/1492857.
@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},
issn = {0196-2892},
number = 6,
volume = 57,
place = {United States},
year = {2018},
month = {12}
}

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