A comparison of template matching and deep learning for classification of occluded targets in LiDAR data

Isaac Zachmann; Theresa Scarnati

doi:10.1117/12.2556392

29 April 2020 A comparison of template matching and deep learning for classification of occluded targets in LiDAR data

Isaac Zachmann, Theresa Scarnati

Proceedings Volume 11394, Automatic Target Recognition XXX; 113940H (2020) https://doi.org/10.1117/12.2556392
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Automatic target recognition (ATR) is an ongoing topic of research for the Air Force. In this effort we develop, analyze and compare template matching and deep learning algorithms for use in the task of classifying occluded targets in light detection and ranging (LiDAR) data. Specifically, we analyze convolutional sparse representations (CSR) and convolutional neural networks (CNN). We explore the strengths and weaknesses of each algorithm separately, then improve the algorithms, and finally provide a comprehensive comparison of the developed tools. To conduct this final comparison, we improve the functionality of current LiDAR simulators to include our occlusion creator and parallelize our data simulation tools for use on the DoD High Performance Computers. Our results demonstrate that for this problem, a DenseNet trained with images containing representative clutter outperforms a basic CNN and the CSR approach.

Conference Presentation

Citation Download Citation

Isaac Zachmann and Theresa Scarnati "A comparison of template matching and deep learning for classification of occluded targets in LiDAR data", Proc. SPIE 11394, Automatic Target Recognition XXX, 113940H (29 April 2020); https://doi.org/10.1117/12.2556392

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