Publications Details

Paired neural networks for hyperspectral target detection

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

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.