A challenging problem of major importance in hyperspectral imaging applications is the detection of subpixel objects of military and civilian interest. High false alarm thresholds are required to detect subpixel objects due to the large amount of surrounding background clutter. These high false alarm rates are unacceptable for military purposes, requiring the need for false alarm mitigation (FAM) techniques to weed out the objects of interest. The objective of this paper is to provide a comparison of the implementation of these FAM techniques and their inherent benefits in the whitened detection space. The widely utilized matched filter (MF) and adaptive cosine estimator (ACE) are both based on a linear mixing model (LMM) between a background and object class. The matched filter approximates the object abundance, and the ACE measures the model error. Each of these measurements provides inadequate object separation alone, but by using both the object abundance and model error, the objects can be separated from the false alarms.

Date Published: 18 May 2013
PDF: 12 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 874304 (18 May 2013); doi: 10.1117/12.2015906

Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)