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.

Infrared object classification with a hybrid optical convolution neural network

Jianbo Chen, Jennifer Talley, and Kevin F. Kelly
Appl. Opt. 60(25) G224-G231 (2021)

Exo-atmospheric infrared objects classification using recurrence-plots-based convolutional neural networks

Qiuqun Deng, Huanzhang Lu, Moufa Hu, and Bendong Zhao
Appl. Opt. 58(1) 164-171 (2019)

Single-pixel neural network object classification of sub-Nyquist ghost imaging

Jia-Ning Cao, Yu-Hui Zuo, Hua-Hua Wang, Wei-Dong Feng, Zhi-Xin Yang, Jian Ma, Hao-Ran Du, Lu Gao, and Ze Zhang
Appl. Opt. 60(29) 9180-9187 (2021)

Assisting target recognition through strong turbulence with the help of neural networks

Kathleen Kee, Chensheng Wu, Daniel A. Paulson, and Christopher C. Davis
Appl. Opt. 59(30) 9434-9442 (2020)

Convolutional neural network for estimating physical parameters from Newton’s rings

Peihang Li, Ming-Feng Lu, Chen-Chen Ji, Jin-Min Wu, Zhe Liu, Chenyang Wang, Feng Zhang, and Ran Tao
Appl. Opt. 60(13) 3964-3970 (2021)