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

Journal Article · Thu Oct 01 04:00:00 EDT 2015 · Sandia journal manuscript; Not yet accepted for publication

OSTI ID:1427269

Koch, Mark William ^[1]; Steinbach, Ryan Matthew ^[1]; Moya, Mary M ^[1]

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

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.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1427269
• Report Number(s): SAND--2015-9431J; 607846
• Journal Information: Sandia journal manuscript; Not yet accepted for publication, Journal Name: Sandia journal manuscript; Not yet accepted for publication; ISSN 9999-0014
• Publisher: Sandia
• Country of Publication: United States
• Language: English