As an adjacent planet with profound connections to the Earth in cosmic evolution, the semantic segmentation of Martian surface geomorphological features not only facilitates the construction of a cognitive framework for understanding the dynamic formation and evolutionary mechanisms at the planetary scale but also establishes a multifaceted research paradigm in the field of planetary science. This holds particular significance in refining the theoretical framework of planetary evolution and validating astrophysical models, thereby possessing critical scientific value. However, the analysis of Martian surface imagery encounters multifaceted technical challenges primarily characterized by complex and variable lighting conditions, low structural degree of topographical features, and pronounced heterogeneity in target scale distribution. Collectively, these characteristics form key technological bottlenecks in the intelligent interpretation of planetary surfaces. To address these issues, this paper proposes a Mars surface image segmentation algorithm based on dynamic ternary attention mechanism. Our approach synergistically optimizes adaptive feature fusion and dynamic attention mechanisms to enhance segmentation accuracy. First, we develop a dynamic ternary attention module that automatically adjusts branch significance weights, enabling dynamic focus on local and global features for typical Martian landforms like rocks and dunes. Second, an adaptive bidirectional feature fusion module is designed to reconcile spatial and semantic information conflicts across scales. Moreover, a channel-attentive separable convolution is proposed to reduce parameter complexity while enhancing model generalization capabilities. Experimental results demonstrate that the proposed algorithm achieves 89.06% accuracy and 72.33% mean intersection over union on the S5Mars dataset,effectively extracting and integrating multi-scale features to significantly enhance segmentation precision for Martian surface imagery.