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Proceedings Paper

Paired neural networks for hyperspectral target detection
Author(s): Dylan Z. Anderson; Joshua D. Zollweg; Braden J. Smith
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Paper Abstract

Spectral matched filtering and its variants (e.g. Adaptive Coherence Estimator or ACE) rely on strong assumptions about target and background distributions. For instance, ACE assumes a Gaussian distribution of background and additive target model. In practice, natural spectral variation, due to effects such as material Bidirectional Reflectance Distribution Function, non-linear mixing with surrounding materials, or material impurities, degrade the performance of matched filter techniques and require an ever-increasing library of target templates measured under different conditions. In this work, we employ the contrastive loss function and paired neural networks to create data-driven target detectors that do not rely on strong assumptions about target and background distribution. Furthermore, by matching spectra to templates in a highly nonlinear fashion via neural networks, our target detectors exhibit improved performance and greater resiliency to natural spectral variation; this performance improvement comes with no increase in target template library size. We evaluate and compare our paired neural network detector to matched filter-based target detectors on a synthetic hyperspectral scene and the well-known Indian Pines AVIRIS hyperspectral image.

Paper Details

Date Published: 6 September 2019
PDF: 11 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390J (6 September 2019);
Show Author Affiliations
Dylan Z. Anderson, Sandia National Labs. (United States)
Joshua D. Zollweg, Sandia National Labs. (United States)
Braden J. Smith, Sandia National Labs. (United States)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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