Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms

Charlene E. Caefer; M. Stefanou; E. D. Nielsen; Anthony Rizzuto; Ori Raviv; Stanley R. Rotman

doi:10.1117/1.2759894

1 July 2007 Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms

Charlene E. Caefer, M. Stefanou, E. D. Nielsen, Anthony Rizzuto, Ori Raviv, Stanley R. Rotman

Author Affiliations +

Optical Engineering, Vol. 46, Issue 7, 076402 (July 2007). https://doi.org/10.1117/1.2759894

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.

©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Charlene E. Caefer, M. Stefanou, E. D. Nielsen, Anthony Rizzuto, Ori Raviv, and Stanley R. Rotman "Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms," Optical Engineering 46(7), 076402 (1 July 2007). https://doi.org/10.1117/1.2759894

Published: 1 July 2007

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