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

Authors : Ahmet Faruk Pala, Gülşah Karaduman, Erdal Özbay

Pages : 300-310

Doi:10.18586/msufbd.1726149

View : 67 | Download : 91

Publication Date : 2025-12-24

Article Type : Research Paper

Abstract :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.
Keywords : : Derin öğrenme, EfficientNetB4, Grad-CAM Mars yüzeyi, Sınıflandırma, Transfer öğrenme

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