By Topic

Multiparameter Receiver Operating Characteristic Analysis for Signal Detection and Classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Chein-I Chang ; Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA

Receiver operating characteristic (ROC) analysis is a widely used evaluation tool in signal processing and communications, and medical diagnosis for performance analysis. It utilizes 2-D curves plotted by detection rate (P D) against false alarm rate (P F) to assess effectiveness of a detector, sensor/device for detection. However, P D and P F are actually dependent parameters resulting from a more crucial but implicit parameter hidden in the ROC curves, threshold ¿ , which is determined by the cost of implementing a detector or sensor/device, except only the case that when the Bayes theory is used for detection, ¿ is completely determined by the Bayes cost. This paper extends the traditional ROC analysis for single-signal detection to detection and classification of multiple signals. It also explores relationships among the three parameters, P D, P F, and ¿ , and further develops a new concept of multiparameter ROC analysis, which uses 3-D ROC curves plotted by three parameters, P D, P F, and ¿, to evaluate effectiveness of detection performance based on interrelationship among P D, P F, and ¿, rather then only P D and P F used by 2-D ROC analysis. As a result of a 3-D ROC curve, three 2-D ROC curves can be also derived: the conventional 2-D ROC curve plotted by P D versus P F and two new 2-D ROC curves plotted based on P D versus ¿ and P F versus ¿. In order to demonstrate the utility of 3-D ROC analysis, four applications are considered: hyperspectral target detection, medical diagnosis, chemical/biological agent detection, and biometric recognition.

Published in:

IEEE Sensors Journal  (Volume:10 ,  Issue: 3 )