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.

翻译：火星表面的分割与解读在火星探测中具有关键作用，可为巡视器的轨迹规划与避障提供必要数据。然而，复杂的地形、相似的地表特征以及大规模标注数据的匮乏，给高精度火星表面语义分割带来了重大挑战。为应对这些挑战，我们提出了一种基于编码器-解码器的新型火星分割网络，命名为MarsSeg。具体而言，我们采用最小化下采样层数量的编码器-解码器结构以保留局部细节。为促进跨多级阴影特征图的高层语义理解，我们在编码器与解码器之间引入特征增强连接层，该层融合了微型空洞空间金字塔池化（Mini-ASPP）、极化自注意力（PSA）以及条状金字塔池化模块（SPPM）。其中Mini-ASPP与PSA专门用于阴影特征增强，从而实现对局部细节与小目标的表达；而SPPM则用于深层特征增强，助力提取与高层语义类别相关的信息。基于Mars-Seg与AI4Mars数据集的实验结果证实，所提出的MarsSeg在分割性能上优于其他最先进方法，并验证了各提出模块的有效性。