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Proceedings Paper

Solid target spectral variability in LWIR
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Paper Abstract

We continue to highlight the pattern recognition challenges associated with solid target spectral variability in the longwave infrared (LWIR) region of the electromagnetic spectrum for a persistent imaging experiment. The experiment focused on the collection and exploitation of LWIR hyperspectral imagery. We propose two methods for target detection, one based on the repeated-random-sampling trial adaptation to a single-class version of support vector machine, and the other based on a longitudinal data model. The defining characteristic of a longitudinal study is that objects are measured repeatedly through time and, as a result, data are dependent. This is in contrast to cross-sectional studies in which the outcomes of a specific event are observed by randomly sampling from a large population of relevant objects in which data are assumed independent. Researchers in the remote sensing community generally assume the problem of object recognition to be cross-sectional. Performance contrast is quantified using a LWIR hyperspectral dataset acquired during three consecutive diurnal cycles, and results reinforce the need for using data models that are more realistic to LWIR spectral data.

Paper Details

Date Published: 17 May 2016
PDF: 10 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400Q (17 May 2016); doi: 10.1117/12.2222939
Show Author Affiliations
Dalton Rosario, U.S. Army Research Lab. (United States)
Christoph Borel, U.S. Army Research Lab. (United States)
Joao Romano, U.S. Army Armament Research, Development and Engineering Ctr. (United States)


Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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