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Proceedings Paper

Transductive and matched-pair machine learning for difficult target detection problems
Author(s): James Theiler
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Paper Abstract

This paper will describe the application of two non-traditional kinds of machine learning (transductive machine learning and the more recently proposed matched-pair machine learning) to the target detection problem. The approach combines explicit domain knowledge to model the target signal with a more agnostic machine-learning approach to characterize the background. The concept is illustrated with simulated data from an elliptically-contoured background distribution, on which a subpixel target of known spectral signature but unknown spatial extent has been implanted.

Paper Details

Date Published: 13 June 2014
PDF: 9 pages
Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 90880E (13 June 2014); doi: 10.1117/12.2048860
Show Author Affiliations
James Theiler, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 9088:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX
Miguel Velez-Reyes; Fred A. Kruse, Editor(s)

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