  Precise aircraft recognition in low-resolution remote sensing imagery is a
challenging yet crucial task in aviation, especially combat identification.
This research addresses this problem with a novel, scalable, and AI-driven
solution. The primary hurdle in combat identification in remote sensing imagery
is the accurate recognition of Novel/Unknown types of aircraft in addition to
Known types. Traditional methods, human expert-driven combat identification and
image classification, fall short in identifying Novel classes. Our methodology
employs similarity learning to discern features of a broad spectrum of military
and civilian aircraft. It discerns both Known and Novel aircraft types,
leveraging metric learning for the identification and supervised few-shot
learning for aircraft type classification. To counter the challenge of limited
low-resolution remote sensing data, we propose an end-to-end framework that
adapts to the diverse and versatile process of military aircraft recognition by
training a generalized embedder in fully supervised manner. Comparative
analysis with earlier aircraft image classification methods shows that our
approach is effective for aircraft image classification (F1-score Aircraft Type
of 0.861) and pioneering for quantifying the identification of Novel types
(F1-score Bipartitioning of 0.936). The proposed methodology effectively
addresses inherent challenges in remote sensing data, thereby setting new
standards in dataset quality. The research opens new avenues for domain experts
and demonstrates unique capabilities in distinguishing various aircraft types,
contributing to a more robust, domain-adapted potential for real-time aircraft
recognition.
