Enhancing Martian Terrain Recognition With Deep Constrained Clustering

Martian terrain recognition is pivotal for advancing our understanding of topography, geomorphology, paleoclimate, and habitability. While deep clustering methods have shown promise in learning semantically homogeneous feature embeddings from Martian rover imagery, the natural variations in intensity, scale, and rotation pose significant challenges for accurate terrain classification. To address these limitations, we propose deep constrained clustering with metric learning (DCCML), a novel algorithm that leverages multiple constraint types to guide the clustering process. DCCML incorporates soft must-link constraints derived from spatial and depth similarities between neighboring patches, alongside hard constraints from stereo camera pairs and temporally adjacent images. Experimental evaluation on the Curiosity rover dataset (using 150 clusters) demonstrates that DCCML increases homogeneous clusters by 16.7% while reducing the Davies-Bouldin index from 3.86 to 1.82 and boosting retrieval accuracy from 86.71% to 89.86%. This improvement enables a more precise classification of Martian geological features, advancing our capacity to analyze and understand the planet’s landscape.