RESUMO
In a recent paper, Kee et al. [Appl. Opt.59, 9434 (2020)APOPAI0003-693510.1364/AO.405663] 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.
