An information measure of sensor performance and its relation to the ROC curve
2010 (English)In: Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI / [ed] Sylvia S. Shen; Paul E. Lewis, SPIE - International Society for Optical Engineering, 2010, Art.nr. 7695-72- p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2010. Art.nr. 7695-72- p.
, Proceedings of SPIE, 7695
ROC, information theory, target detection, hyperspectral, sensor performance
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-120554DOI: 10.1117/12.851322OAI: oai:DiVA.org:liu-120554DiVA: diva2:846258
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, Orlando, Florida, USA, 5–8 April 2010