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Title: Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers

Abstract

Except in the most extreme conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. A SAR can provide surveillance over a long time period by making multiple passes over a wide area. For object-based intelligence it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. Using superpixels and their first two moments we develop a series of one-class classification algorithms using a goodness-of-fit metric. P-value fusion is used to combine the results from different classes. We also show how to combine multiple one-class classifiers to get a confidence about a classification. This can be used by downstream algorithms such as a conditional random field to enforce spatial constraints.

Authors:
 [1];  [1];  [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:
1427269
Report Number(s):
SAND-2015-9431J
Journal ID: ISSN 9999-0014; 607846
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Journal Article
Resource Relation:
Journal Name: Sandia journal manuscript; Not yet accepted for publication
Country of Publication:
United States
Language:
English

Citation Formats

Koch, Mark William, Steinbach, Ryan Matthew, and Moya, Mary M. Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers. United States: N. p., 2015. Web.
Koch, Mark William, Steinbach, Ryan Matthew, & Moya, Mary M. Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers. United States.
Koch, Mark William, Steinbach, Ryan Matthew, and Moya, Mary M. Thu . "Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers". United States. doi:. https://www.osti.gov/servlets/purl/1427269.
@article{osti_1427269,
title = {Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers},
author = {Koch, Mark William and Steinbach, Ryan Matthew and Moya, Mary M},
abstractNote = {Except in the most extreme conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. A SAR can provide surveillance over a long time period by making multiple passes over a wide area. For object-based intelligence it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. Using superpixels and their first two moments we develop a series of one-class classification algorithms using a goodness-of-fit metric. P-value fusion is used to combine the results from different classes. We also show how to combine multiple one-class classifiers to get a confidence about a classification. This can be used by downstream algorithms such as a conditional random field to enforce spatial constraints.},
doi = {},
journal = {Sandia journal manuscript; Not yet accepted for publication},
number = ,
volume = ,
place = {United States},
year = {Thu Oct 01 00:00:00 EDT 2015},
month = {Thu Oct 01 00:00:00 EDT 2015}
}