Planetary Scene Classification via a Novel Zero-Shot Hierarchical State-Space Model

Abstract

Planetary scene classification plays a fundamental role in geomorphological analysis and autonomous exploration missions. However, planetary terrains exhibit high intraclass structural variability, and their analysis relies on an extremely limited set of annotated samples, making exhaustive premission labeling impractical. Therefore, recognition systems should be able to identify unseen classes with few (and sometimes no) training examples. This naturally motivates the adoption of the zero-shot learning (ZSL) paradigm for planetary scene classification. Existing solutions either require large-scale pretraining or employ heterogeneous and attention-intensive designs, limiting their practicality in data-scarce and resource-constrained planetary environments. To address these issues, we propose HiL-SSM, a hierarchical interactive linear state-space modeling framework for zero-shot planetary scene classification. It employs a unified, attention-free architecture based on structured state-space models (SSMs), enabling joint optimization of visual representation learning and semantic alignment within a single backbone framework. Importantly, it does not rely on large-scale pretraining. A hierarchical stage-wise interaction mechanism is introduced to progressively refine visual–semantic correspondence across multiple representation levels, enabling stronger alignment between geomorphological structures and semantic descriptors. Experiments on the ZSMars dataset demonstrate that the proposed framework achieves favorable classification performance under multiple seen/unseen splits while balancing computational complexity and accuracy.