Fast, accurate and robust automatic target recognition (ATR) in optical aerial imagery can provide game-changing
advantages to military commanders and personnel. ATR algorithms must reject non-targets with a high degree of
confidence in a world with an infinite number of possible input images. Furthermore, they must learn to recognize new
targets without requiring massive data collections. Whereas most machine learning algorithms classify data in a closed set
manner by mapping inputs to a fixed set of training classes, open set recognizers incorporate constraints that allow for
inputs to be labelled as unknown. We have adapted two template-based open set recognizers to use computer generated
synthetic images of military aircraft as training data, to provide a baseline for military-grade ATR: (1) a frequentist
approach based on probabilistic fusion of extracted image features, and (2) an open set extension to the one-class support
vector machine (SVM). These algorithms both use histograms of oriented gradients (HOG) as features as well as artificial
augmentation of both real and synthetic image chips to take advantage of minimal training data. Our results show that
open set recognizers trained with synthetic data and tested with real data can successfully discriminate real target inputs
from non-targets. However, there is still a requirement for some knowledge of the real target in order to calibrate the
relationship between synthetic template and target score distributions. We conclude by proposing algorithm modifications
that may improve the ability of synthetic data to represent real data.
Aleksander B. Bapst, Jonathan Tran, Mark W. Koch, Mary M. Moya, and Robert Swahn, "Open set recognition of aircraft in aerial imagery using synthetic template models," Proc. SPIE 10202, Automatic Target Recognition XXVII, 1020206 (Presented at SPIE Defense + Security: April 10, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2262150.
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