Polarimetric Interferometric SAR Change Detection Discrimination

Journal Article · Thu Dec 13 23:00:00 EST 2018 · IEEE Transactions on Geoscience and Remote Sensing

DOI:https://doi.org/10.1109/TGRS.2018.2879787· OSTI ID:1492857

West, Roger Derek ^[1]; Riley, Robert M. ^[1]

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

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.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000
• OSTI ID: 1492857
• Report Number(s): SAND--2018-12632J; 668710
• Journal Information: IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Journal Issue: 6 Vol. 57; ISSN 0196-2892
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

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