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Comparison of neural network classifiers for automatic target recognition
Author(s): Mark Carlotto; Mark Nebrich; David Ramirez
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Paper Abstract

We consider a challenge problem involving the automatic detection of large commercial vehicles such as trucks, buses, and tractor-trailers in Quickbird EO pan imagery. Three target classifiers are evaluated: a “bagged” perceptron algorithm (BPA) that uses an ensemble method known as bootstrap aggregation to increase classification performance, a convolutional neural network (CNN) implemented using the MobileNet architecture in TensorFlow, and a memory-based classifier (MBC), which also uses bagging to increase performance. As expected, the CNN significantly outperformed the BPA. Surprisingly, the performance of the MBC was only slightly below that of the CNN. We discuss these results and their implications for this and other similar applications.

Paper Details

Date Published: 7 May 2019
PDF: 8 pages
Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 1101810 (7 May 2019); doi: 10.1117/12.2519310
Show Author Affiliations
Mark Carlotto, General Dynamics Mission Systems (United States)
Mark Nebrich, General Dynamics Mission Systems (United States)
David Ramirez, General Dynamics Mission Systems (United States)


Published in SPIE Proceedings Vol. 11018:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII
Ivan Kadar; Erik P. Blasch; Lynne L. Grewe, Editor(s)

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