Polarimetric SAR Image Terrain Classification

West, R. Derek; LaBruyere III, Thomas E.; Skryzalin, Jacek; Simonson, Katherine M.; Hansen, Ross L.; Van Benthem, Mark H.

doi:10.1109/jstars.2019.2946768

Title: Polarimetric SAR Image Terrain Classification

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Abstract

In practical applications of automated terrain classification from high-resolution polarimetric synthetic aperture radar (PolSAR) imagery, different terrain types may inherently contain a high level of internal variability, as when a broadly defined class (e.g., “trees”) contains elements arising from multiple subclasses (pine, oak, and willow). In addition, real-world factors such as the time of year of a collection, the moisture content of the scene, the imaging geometry, and the radar system parameters can all increase the variability observed within each class. Such variability challenges the ability of classifiers to maintain a high level of sensitivity in recognizing diverse elements that are within-class, without sacrificing their selectivity in rejecting out-of-class elements. In an effort to gauge the degree to which classifiers respond robustly in the presence of intraclass variability and generalize to untrained scenes and conditions, here we compare the performance of a suite of classifiers across six broad terrain categories from a large set of polarimetric synthetic aperture radar (PolSAR) image sets. The main contributions of this article are as follows: 1) an analysis of the robustness of a variety of current state-of-the art classification algorithms to intraclass variability found in PolSAR image sets, and 2) the associated PolSAR imagemore »« less

Authors:

^[1]; LaBruyere III, Thomas E. ^[2];

^[1];

^[1]; Hansen, Ross L. ^[3]; Van Benthem, Mark H. ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Amazon.com Inc., Seattle, WA (United States)

Publication Date:: Wed Nov 13 00:00:00 EST 2019

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

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

OSTI Identifier:: 1697988

Report Number(s):: SAND-2019-12177J
Journal ID: ISSN 1939-1404; 680205

Grant/Contract Number:: AC04-94AL85000; NA0003525

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Additional Journal Information:: Journal Volume: 12; Journal Issue: 11; Journal ID: ISSN 1939-1404

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 42 ENGINEERING; polarimetric SAR (PolSAR); machine learning; wishart; terrain classification

Citation Formats


                    West, R. Derek, LaBruyere III, Thomas E., Skryzalin, Jacek, Simonson, Katherine M., Hansen, Ross L., and Van Benthem, Mark H. Polarimetric SAR Image Terrain Classification.  United States: N. p., 2019. 
Web.  doi:10.1109/jstars.2019.2946768.

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                    West, R. Derek, LaBruyere III, Thomas E., Skryzalin, Jacek, Simonson, Katherine M., Hansen, Ross L., & Van Benthem, Mark H. Polarimetric SAR Image Terrain Classification.  United States.  https://doi.org/10.1109/jstars.2019.2946768

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                    West, R. Derek, LaBruyere III, Thomas E., Skryzalin, Jacek, Simonson, Katherine M., Hansen, Ross L., and Van Benthem, Mark H. Wed .  
"Polarimetric SAR Image Terrain Classification".  United States.  https://doi.org/10.1109/jstars.2019.2946768.  https://www.osti.gov/servlets/purl/1697988.

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@article{osti_1697988,

  title        = {Polarimetric SAR Image Terrain Classification},

  author       = {West, R. Derek and LaBruyere III, Thomas E. and Skryzalin, Jacek and Simonson, Katherine M. and Hansen, Ross L. and Van Benthem, Mark H.},

  abstractNote = {In practical applications of automated terrain classification from high-resolution polarimetric synthetic aperture radar (PolSAR) imagery, different terrain types may inherently contain a high level of internal variability, as when a broadly defined class (e.g., “trees”) contains elements arising from multiple subclasses (pine, oak, and willow). In addition, real-world factors such as the time of year of a collection, the moisture content of the scene, the imaging geometry, and the radar system parameters can all increase the variability observed within each class. Such variability challenges the ability of classifiers to maintain a high level of sensitivity in recognizing diverse elements that are within-class, without sacrificing their selectivity in rejecting out-of-class elements. In an effort to gauge the degree to which classifiers respond robustly in the presence of intraclass variability and generalize to untrained scenes and conditions, here we compare the performance of a suite of classifiers across six broad terrain categories from a large set of polarimetric synthetic aperture radar (PolSAR) image sets. The main contributions of this article are as follows: 1) an analysis of the robustness of a variety of current state-of-the art classification algorithms to intraclass variability found in PolSAR image sets, and 2) the associated PolSAR image and feature data that Sandia is releasing to the research community with this publication. The analysis of the classification algorithms we provide will serve as a benchmark of performance for the future PolSAR terrain classification algorithm research and development enabled by the image sets and data provided. By sharing our analysis and high-resolution fully polarimetric Sandia data with the research community, we enable others to develop and assess a new generation of robust terrain classification algorithms for PolSAR.},

  doi          = {10.1109/jstars.2019.2946768},

  journal      = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},

  number       = 11,

  volume       = 12,

  place        = {United States},

  year         = {Wed Nov 13 00:00:00 EST 2019},

  month        = {Wed Nov 13 00:00:00 EST 2019}

}

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