Open AccessJoint object classification and turbulence strength estimation using convolutional neural networks
Author(s) -
Daniel A. LeMaster,
Steven Leung,
Olga Mendoza-Schrock
Publication year - 2021
Publication title -
applied optics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.668
H-Index - 197
eISSN - 2155-3165
pISSN - 1559-128X
DOI - 10.1364/ao.425119
Subject(s) - convolutional neural network , computer science , artificial neural network , estimator , artificial intelligence , turbulence , atmospheric turbulence , classifier (uml) , multilayer perceptron , pattern recognition (psychology) , physics , mathematics , statistics , meteorology
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.