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

Charlene E. Caefer, Marcus S. Stefanou, Eric D. Nielsen, Anthony P. Rizzuto, Ori Raviv, Stanley R. Rotman

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

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.

Original languageEnglish
Article number076402
JournalOptical Engineering
Volume46
Issue number7
DOIs
StatePublished - 1 Jul 2007

Keywords

  • Algorithm performance metric
  • Hyperspectral
  • Point target detection

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • General Engineering

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