Abstract
As a step in a Dynamic Data-Driven Applications Systems (DDDAS) method to characterize the background in a vehicle tracking problem, we extend the application of deep learning to a hyperspectral dataset (the AeroRIT dataset) to evaluating network uncertainty. Expressing uncertainty information is crucial for evaluating what additional information is needed in the DDDAS algorithm and where more resources are required. Hyperspectral signatures tend to be very noisy, when captured from an aerial flight and a slight shift in the atmospheric conditions can alter the signals significantly, which in turn may affect the trained network’s classifications. In this work, we apply Deep Ensembles, Monte Carlo Dropout and Batch Ensembles and study their effects with respect to achieving robust pixel-level identifications by expressing the uncertainty within the trained networks on the task of semantic segmentation. We modify the U-Net-m architecture from the AeroRIT paper to account for the frameworks and present our results as a step towards accounting for sensitive changes in hyperspectral signals.
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Acknowledgements
This work was supported by the Dynamic Data Driven Applications Systems Program, Air Force Office of Scientific Research under Grant FA9550-19-1-0021. We gratefully acknowledge the support of NVIDIA Corporation with the donations of the Titan X and Titan Xp Pascal GPUs used for this research.
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Rangnekar, A., Ientilucci, E., Kanan, C., Hoffman, M.J. (2020). Uncertainty Estimation for Semantic Segmentation of Hyperspectral Imagery. In: Darema, F., Blasch, E., Ravela, S., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2020. Lecture Notes in Computer Science(), vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_20
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