Semantic segmentation of Martian terrain is crucial for the route planning and autonomous navigation of rovers on Mars. However, existing methods are restricted to structured or semi-structured scenes, performing poorly on Mars that is a completely unstructured environment. Therefore, we propose a novel hybrid attention semantic segmentation (HASS) network, which contains a global intra-class attention branch, a local inter-class attention branch and a representation merging module. Specifically, the global attention branch draws the consistencies of all homogeneous pixels in the whole image, and the local attention branch models the relationships between specific heterogeneous pixels with the supervision of elaborately designed loss function. The merging module aggregates the contexts from the two branches for the final segmentation. Furthermore, we establish a panorama semantic segmentation dataset of Martian landforms, named MarsScapes, which provides fine-grained annotations for eight semantic categories. Extensive experiments on our MarsScapes and the public AI4Mars datasets show the superiority of the proposed method.

火星地形的语义分割对于火星探测器的路线规划和自主导航至关重要。然而，现有方法仅限于结构化或半结构化场景，在完全非结构化环境的火星上表现不佳。因此，我们提出了一种新的混合注意力语义分割（HASS）网络，它包含一个全局类内注意力分支、一个局部类间注意力分支和一个表示合并模块。具体来说，全局注意力分支绘制整幅图像中所有同质像素的一致性，局部注意力分支在精心设计的损失函数的监督下对特定异构像素之间的关系进行建模。合并模块聚合来自两个分支的上下文以进行最终分割。此外，我们建立了一个火星地貌的全景语义分割数据集，名为 MarsScapes，它为八个语义类别提供了细粒度的注释。在我们的 MarsScapes 和公共 AI4Mars 数据集上进行的大量实验表明了所提出方法的优越性。