Paired neural networks for hyperspectral target detection

Dylan Z. Anderson; Joshua D. Zollweg; Braden J. Smith

doi:10.1117/12.2531310

6 September 2019 Paired neural networks for hyperspectral target detection

Dylan Z. Anderson, Joshua D. Zollweg, Braden J. Smith

Proceedings Volume 11139, Applications of Machine Learning; 111390J (2019) https://doi.org/10.1117/12.2531310
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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.

Citation Download Citation

Dylan Z. Anderson, Joshua D. Zollweg, and Braden J. Smith "Paired neural networks for hyperspectral target detection", Proc. SPIE 11139, Applications of Machine Learning, 111390J (6 September 2019); https://doi.org/10.1117/12.2531310

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