  The segmentation and interpretation of the Martian surface play a pivotal
role in Mars exploration, providing essential data for the trajectory planning
and obstacle avoidance of rovers. However, the complex topography, similar
surface features, and the lack of extensive annotated data pose significant
challenges to the high-precision semantic segmentation of the Martian surface.
To address these challenges, we propose a novel encoder-decoder based Mars
segmentation network, termed MarsSeg. Specifically, we employ an
encoder-decoder structure with a minimized number of down-sampling layers to
preserve local details. To facilitate a high-level semantic understanding
across the shadow multi-level feature maps, we introduce a feature enhancement
connection layer situated between the encoder and decoder. This layer
incorporates Mini Atrous Spatial Pyramid Pooling (Mini-ASPP), Polarized
Self-Attention (PSA), and Strip Pyramid Pooling Module (SPPM). The Mini-ASPP
and PSA are specifically designed for shadow feature enhancement, thereby
enabling the expression of local details and small objects. Conversely, the
SPPM is employed for deep feature enhancement, facilitating the extraction of
high-level semantic category-related information. Experimental results derived
from the Mars-Seg and AI4Mars datasets substantiate that the proposed MarsSeg
outperforms other state-of-the-art methods in segmentation performance,
validating the efficacy of each proposed component.
