Transductive and matched-pair machine learning for difficult target detection problems

James Theiler

doi:10.1117/12.2048860

13 June 2014 Transductive and matched-pair machine learning for difficult target detection problems

James Theiler

Author Affiliations +

Proceedings Volume 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX; 90880E (2014) https://doi.org/10.1117/12.2048860
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

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.

Citation Download Citation

James Theiler "Transductive and matched-pair machine learning for difficult target detection problems", Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 90880E (13 June 2014); https://doi.org/10.1117/12.2048860

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