EfficientNetB4 Tabanlı Mars Yüzeyi Görüntülerinin Sınıflandırılması: Transfer Öğrenme, Veri Artırma ve Grad-CAM ile Açıklanabilirlik Analizi

Öz

Mars yüzeyinde yer alan jeolojik yapıların doğru biçimde sınıflandırılması, gezegen bilimi çalışmalarının ilerlemesi ve otonom keşif sistemlerinin geliştirilmesi açısından kritik bir öneme sahiptir. Bu çalışmada, Mars yüzeyine ait sekiz farklı jeolojik yapının (Other, Slope Streak, Spider, Swiss Cheese, Bright Dune, Crater, Dark Dune ve Impact Ejecta) görüntü tabanlı sınıflandırılması için derin öğrenme temelli bir yaklaşım önerilmektedir. Çalışmada, Kaggle platformunda sunulan Mars Terrain Classification veri seti kullanılmış ve EfficientNetB4 mimarisi üzerine inşa edilen bir transfer öğrenme modeli geliştirilmiştir. Model performansını artırmak amacıyla çeşitli veri önişleme ve veri artırma teknikleri uygulanmıştır. Ayrıca, modelin karar verme sürecinin daha iyi anlaşılabilmesi için Grad-CAM tabanlı açıklanabilirlik analizleri gerçekleştirilmiştir. Deneysel sonuçlar, önerilen yöntemin yüksek sınıflandırma başarımı sağladığını ve görselleştirme analizleri sayesinde jeolojik yapıların güvenilir biçimde ayırt edilebildiğini göstermektedir. Elde edilen bulgular, veri verimliliği yüksek ve açıklanabilir derin öğrenme modellerinin Mars yüzeyi analizinde etkili bir çözüm sunduğunu ortaya koymaktadır.

Anahtar Kelimeler

• : Derin öğrenme
• EfficientNetB4
• Grad-CAM Mars yüzeyi
• Sınıflandırma
• Transfer öğrenme

EfficientNetB4 Based Classification of Mars Surface Images: Explainability Analysis with Transfer Learning, Data Augmentation and Grad-CAM

Öz

Accurate classification of geological structures on the Martian surface is of critical importance for advancing planetary science research and developing autonomous exploration systems. In this study, a deep learning–based approach is proposed to classify images of eight different Martian geological structures, namely Other, Slope Streak, Spider, Swiss Cheese, Bright Dune, Crater, Dark Dune, and Impact Ejecta. The Mars Terrain Classification dataset obtained from the Kaggle platform is utilized, and a transfer learning model built upon the EfficientNetB4 architecture is developed. To enhance the model performance, various data preprocessing and data augmentation techniques are applied. Furthermore, Grad-CAM (Gradient-weighted Class Activation Mapping)–based visualization methods are employed to improve the transparency and interpretability of the model’s decision-making process. Experimental results demonstrate that the proposed model achieves high classification accuracy and enables reliable identification of geological structures through explainability analyses. The findings indicate that deep learning models that are both data-efficient and interpretable can provide significant contributions to Martian surface classification, addressing an important gap in the existing literature.

Anahtar Kelimeler

• Deep learning
• EfficientNetB4
• Grad-CAM Mars surface
• Classification
• Transfer learning

Kaynakça

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