Initial study of Schroedinger eigenmaps for spectral target detection

Dorado-Munoz, Leidy P.; Messinger, David W.

doi:10.1117/1.oe.55.8.083101

Initial study of Schroedinger eigenmaps for spectral target detection

Journal Article · Tue Aug 02 04:00:00 EDT 2016 · Optical Engineering

DOI:https://doi.org/10.1117/1.oe.55.8.083101· OSTI ID:1581645

Dorado-Munoz, Leidy P. ^[1]; Messinger, David W. ^[2]

Rochester Inst. of Technology, Rochester, NY (United States); Rochester Institute of Technology
Rochester Inst. of Technology, Rochester, NY (United States)

Spectral target detection refers to the process of searching for a specific material with a known spectrum over a large area containing materials with different spectral signatures. Traditional target detection methods in hyperspectral imagery (HSI) require assuming the data fit some statistical or geometric models and based on the model, to estimate parameters for defining a hypothesis test, where one class (i.e., target class) is chosen over the other classes (i.e., background class). Nonlinear manifold learning methods such as Laplacian eigenmaps (LE) have extensively shown their potential use in HSI processing, specifically in classification or segmentation. Recently, Schroedinger eigenmaps (SE), which is built upon LE, has been introduced as a semisupervised classification method. In SE, the former Laplacian operator is replaced by the Schroedinger operator. The Schroedinger operator includes by definition, a potential term V that steers the transformation in certain directions improving the separability between classes. In this regard, we propose a methodology for target detection that is not based on the traditional schemes and that does not need the estimation of statistical or geometric parameters. This method is based on SE, where the potential term V is taken into consideration to include the prior knowledge about the target class and use it to steer the transformation in directions where the target location in the new space is known and the separability between target and background is augmented. An initial study of how SE can be used in a target detection scheme for HSI is shown here. Inscene pixel and spectral signature detection approaches are presented. Finally, the HSI data used comprise various target panels for testing simultaneous detection of multiple objects with different complexities.

Research Organization:: Rochester Inst. of Technology, Rochester, NY (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)

DOE Contract Number:: NA0002482

OSTI ID:: 1581645

Journal Information:: Optical Engineering, Journal Name: Optical Engineering Journal Issue: 8 Vol. 55; ISSN 0091-3286

Publisher:: SPIE

Country of Publication:: United States

Language:: English

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97 MATHEMATICS AND COMPUTING
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hyperspectral image analysis
spectral graph theory
spectral target detection

Initial study of Schroedinger eigenmaps for spectral target detection

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