In a two class label scenario, classi…cation systems may be used to assess whether or not an element of interest belongs to the “target”or “non-target”class. The performance of the system is summarized visually as a trade- o¤ between the proportions of elements correctly labeled as “target”plotted against the proportion of elements incorrectly labeled as “target.” These proportions are empirical estimates of the true and false positive rates, and their trade-o¤ plot is known as a receiver operating characteristic (ROC) curve. Classi…cation performance can be increased, however, if the information provided by multiple systems can be fused together to create a new, combined system. This research focuses on label-fusion as a common method to increase classi…cation performance and quantifying the bias that occurs when misspecifying the partitioning of the underlying event set. This partitioning will be de…ned in terms of what be called within and across label fusion. When incorrect assumptions are made about the partitioning of the event set, bias will occur and both the ROC curve and its optimal parameters will be incorrectly quanti…ed. In this work, we analyze the e¤ects of individual classi…cation system performance, correlation, and target environment on the magnitude of this performance bias. This work will then inspire the development of formulas to adjust optimal performance measures to appropriately re‡ect the fused system performance according to event set partitioning. As such, bias may be appropriately adjusted without redesigning the fused system, allowing greater use of currently fused systems across multiple platforms and environments.

Date Published: 2 May 2017
PDF: 12 pages
Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102000G (2 May 2017);

Published in SPIE Proceedings Vol. 10200:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI
Ivan Kadar, Editor(s)