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17 May 2016 Solid target spectral variability in LWIR
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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.
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Dalton Rosario, Christoph Borel, and Joao Romano "Solid target spectral variability in LWIR", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400Q (17 May 2016); https://doi.org/10.1117/12.2222939
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