Deep Learning-Based Classification of Military Aircraft Types from Satellite Imagery

Abstract

Automatic classification of military aircraft in satellite imagery is a challenging problem with a high risk of error due to the limited pixel area of targets, variations in image resolution and illumination conditions, background complexity, and strong visual similarity among classes. In this study, a deep learning approach is proposed that integrates multi-feature extraction with attention-based focusing in order to strengthen the classification decision. The proposed architecture combines features extracted in parallel from two convolutional backbones with different representation capabilities and enhances the influence of discriminative regions through channel and spatial attention mechanisms. The model is trained using transfer learning to reduce the risk of overfitting caused by the limited number of samples. The method was evaluated on the MTARSI dataset, which contains 20 aircraft classes. Experimental findings show that the proposed approach achieved 99.79% accuracy on the test set and delivered similarly consistent performance in terms of precision, recall, and F1-score. Confusion matrix analysis further reveals that the misclassification rates remained low, particularly among visually similar platform types, and that the attention mechanism contributed meaningfully to inter-class discriminability. These results demonstrate that attention-enhanced multi-backbone representations provide an effective and practical solution for military aircraft recognition applications from satellite imagery.

Keywords

• Military aircraft classification
• attention mechanisms
• deep learning
• convolutional neural networks
• satellite imagery.

Uydu Görüntülerinde Askerî Uçak Tiplerinin Derin Öğrenme Temelli Sınıflandırılması

Abstract

Uydu görüntülerinde askerî uçakların otomatik olarak sınıflandırılması; hedeflerin düşük piksel alanına sahip olması, görüntü çözünürlüğü ve aydınlatma koşullarındaki değişkenlik, arka plan karmaşıklığı ve sınıflar arasındaki yüksek görsel benzerlik nedeniyle hata riskine açık bir problemdir. Bu çalışmada, sınıflandırma kararını güçlendirmek amacıyla çoklu özellik çıkarımı ile dikkat temelli odaklanmayı bütünleştiren bir derin öğrenme yaklaşımı önerilmektedir. Önerilen mimari, farklı temsil yeteneklerine sahip iki evrişimsel omurgadan paralel olarak elde edilen özellikleri bir araya getirmekte ve kanal ile uzamsal dikkat mekanizmaları aracılığıyla ayırt edici bölgelerin etkisini artırmaktadır. Model, transfer öğrenme yaklaşımıyla eğitilerek sınırlı örnek sayısının yol açabileceği aşırı uyum riskini azaltmayı hedeflemektedir. Yöntem, 20 uçak sınıfını içeren MTARSI veri kümesi üzerinde değerlendirilmiştir. Deneysel bulgular, önerilen yaklaşımın test kümesinde %99,79 doğruluk değerine ulaştığını; ayrıca kesinlik, duyarlılık ve F1-skoru metriklerinde de aynı düzeyde tutarlı performans sergilediğini göstermektedir. Karışıklık matrisi analizi, özellikle görsel olarak benzer platform tipleri arasında yanlış sınıflandırma oranlarının düşük kaldığını ve dikkat mekanizmasının sınıflar arası ayırt ediciliğe anlamlı katkı sağladığını ortaya koymaktadır. Elde edilen sonuçlar, uydu görüntülerinden askerî uçak tanıma uygulamalarında dikkat destekli çoklu omurga temsillerinin etkili ve uygulanabilir bir çözüm sunduğunu göstermektedir.

Keywords

• Askerî uçak sınıflandırması
• dikkat mekanizmaları
• derin öğrenme
• evrişimsel sinir ağları
• uydu görüntüleri.

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