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Proceedings Paper

An information measure of sensor performance and its relation to the ROC curve
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Paper Abstract

The ROC curve is the most frequently used performance measure for detection methods and the underlying sensor configuration. Common problems are that the ROC curve does not present a single number that can be compared to other systems and that no discrimination between sensor performance and algorithm performance is done. To address the first problem, a number of measures are used in practice, like detection rate at a specific false alarm rate, or area-under-curve. For the second problem, we proposed in a previous paper1 an information theoretic method for measuring sensor performance. We now relate the method to the ROC curve, show that it is equivalent to selecting a certain point on the ROC curve, and that this point is easily determined. Our scope is hyperspectral data, studying discrimination between single pixels.

Paper Details

Date Published: 13 May 2010
PDF: 8 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769520 (13 May 2010); doi: 10.1117/12.851322
Show Author Affiliations
Jörgen Ahlberg, Swedish Defence Research Agency (Sweden)
Ingmar G. Renhorn, Swedish Defence Research Agency (Sweden)
Niclas Wadströmer, Swedish Defence Research Agency (Sweden)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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