Abstract
In a recent paper, Kee et al. [Appl. Opt. 59, 9434 (2020) [CrossRef] ] use a
multilayer perceptron neural network to classify objects in imagery
after degradation through atmospheric turbulence. They also estimate
turbulence strength when prior knowledge of the object is available.
In this work, we significantly increase the realism of the turbulence
simulation used to train and evaluate the Kee et al. neural network. Second, we develop a new
convolutional neural network for joint character classification and
turbulence strength estimation, thereby eliminating the prior
knowledge constraint. This joint classifier–estimator expands
applicability to a broad range of remote sensing problems, where the
observer cannot access the object of interest directly.
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Data Availability
The training and evaluation imagery used in the study are available in Ref. [24].
24. D. A. LeMaster, S. Leung, and O. L. Mendoza-Schrock, “Joint object classification and
turbulence strength estimation using convolutional neural
networks,” GitHub (2021),
https://github.com/AFRL-RY/Turbulence-Degraded-Characters.
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