Stepwise Deep Feature Transfer Model for Martian Landform Mapping With Small Number of Labeled Samples

The Martian surface landforms are highly related to the safe landing and traversability of Mars rovers. Furthermore, landforms associated with the presence of water/ice, minerals and biosignatures can provide valuable insights for Mars exploration missions, particularly in relation to the selection of landing or sample collection sites. The small number of Martian landform datasets and the scarcity of labelable landform samples over Mars make the precise mapping of Martian landforms a challenging task. In this article, we propose a stepwise deep feature transfer (SDFT) model for the mapping of Martian landforms with a small number of labeled samples. The SDFT model comprises two transfer steps. In the first transfer step, a deep-learning model trained on a large public source dataset from Earth is transferred to a medium sized public dataset from Mars. This transfer is conducted through a standard pretraining and fine-tuning procedure utilizing a linear classifier. In the second transfer step, the model is further transferred to a small number of target datasets on Mars through a pretraining and fine-tuning procedure with a cosine distance classifier. The stepwise training technique mitigates the challenges associated with varying datasets and small training samples. The proposed SDFT model has been validated on two self-built sample sets using images from the Mars Reconnaissance Orbite'rs Context Camera (CTX). It has also been employed for landform mapping in two local regions with small samples to evaluate its effectiveness in comparison with existing state-of-the-art methods.