Multinomial feature matching for
out-of-distribution detection in synthetic aperture
radar
Christopher W. Pitts,
Devin White,
Trilce Estrada,
and Gruia-Catalin Roman
In Automatic Target Recognition XXXVI
2026
National Harbor, Maryland, USA
Received Best Paper Award
Out-of-distribution (OOD) detection is an important
part of automatic target recognition (ATR)
systems. The capability to reject unknown classes
improves reliability and trust in an ATR, and
permits the use of otherwise closed-set classifiers
where open-set recognition is necessary. In this
paper we present multinomial feature matching (MFM),
a method for detecting OOD data in the latent
feature space of neural classifiers, and apply it to
an EfficientNet-B7 model trained on the SAMPLE+
dataset. We show that MFM has efficient and
low-overhead runtime characteristics, and that it
exhibits a high level of performance when applied to
OOD targets in SAMPLE+ and other SAR datasets,
including both vehicle targets and clutter. MFM
achieves a state-of-the-art area under the receiver
operating characteristic (ROC) curve (AUROC) score
and false positive rate (FPR) at a 95% true
positive rate (TPR) (FPR@95) benchmark on the
dataset, outperforming both mainstay benchmarks in
OOD detection and contemporary work in the field.