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Optical Engineering

Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms
Author(s): Charlene E. Caefer; M. Stefanou; E. D. Nielsen; Anthony Rizzuto; Ori Raviv; Stanley R. Rotman
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Paper Abstract

We analyze the efficacy of various point target detection algorithms for hyperspectral data. We present a novel way to measure the discrimination capability of a target detection algorithm; we avoid being critically dependent on the particular placement of a target in the image by examining the overall ability to detect a target throughout the various backgrounds of the cube. We first demonstrate this approach by analyzing previously published algorithms from the literature; we then present two new dissimilar algorithms that are designed to eliminate false alarms on edges. Trade-offs between the probability of detection and false alarms rates are considered. We use our metrics to quantify the improved capability of the proposed algorithms over the standard algorithms.

Paper Details

Date Published: 1 July 2007
PDF: 15 pages
Opt. Eng. 46(7) 076402 doi: 10.1117/1.2759894
Published in: Optical Engineering Volume 46, Issue 7
Show Author Affiliations
Charlene E. Caefer, Air Force Research Lab. (United States)
M. Stefanou, Air Force Research Lab. (United States)
E. D. Nielsen, Air Force Research Lab. (United States)
Anthony Rizzuto, U.S. Air Force (United States)
Ori Raviv, Ben-Gurion Univ. of the Negev (Israel)
Stanley R. Rotman, Ben-Gurion Univ. of the Negev (United States)

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