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7 May 2019 Comparison of neural network classifiers for automatic target recognition
Mark Carlotto, Mark Nebrich, David Ramirez
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Proceedings Volume 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII; 1101810 (2019) https://doi.org/10.1117/12.2519310
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States
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.
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Mark Carlotto, Mark Nebrich, and David Ramirez "Comparison of neural network classifiers for automatic target recognition", Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 1101810 (7 May 2019); https://doi.org/10.1117/12.2519310
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KEYWORDS
Evolutionary algorithms
Image segmentation
3D modeling
Buildings
Object recognition
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