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.

从火星表面的复杂地形、相似的表面特征和缺乏广泛标注数据等方面入手，该研究提出了一种名为 MarsSeg 的新型编码器 - 解码器火星分割网络，利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，并引入位于编码器和解码器之间的特征增强连接层，该层结合 Mini-ASPP、PSA 和 SPPM 等结构，实现遮蔽特征和深层特征的增强，进而在火星分割性能方面优于其他先进方法。

MarsSeg: 使用多级特征提取器和连接器进行火星表面语义分割

MarsSeg: Mars Surface Semantic Segmentation with Multi-level Extractor  and Connector

With the progress of Mars exploration, numerous Mars image data are collected
and need to be analyzed. However, due to the imbalance and distortion of
Martian data, the performance of existing computer vision models is
unsatisfactory. In this paper, we introduce a semi-supervised framework for
machine vision on Mars and try to resolve two specific tasks: classification
and segmentation. Contrastive learning is a powerful representation learning
technique. However, there is too much information overlap between Martian data
samples, leading to a contradiction between contrastive learning and Martian
data. Our key idea is to reconcile this contradiction with the help of
annotations and further take advantage of unlabeled data to improve
performance. For classification, we propose to ignore inner-class pairs on
labeled data as well as neglect negative pairs on unlabeled data, forming
supervised inter-class contrastive learning and unsupervised similarity
learning. For segmentation, we extend supervised inter-class contrastive
learning into an element-wise mode and use online pseudo labels for supervision
on unlabeled areas. Experimental results show that our learning strategies can
improve the classification and segmentation models by a large margin and
outperform state-of-the-art approaches.

本论文介绍了一种半监督机器视觉框架，使用对比学习和注释数据来有效地解决在火星图像数据的分类和分割任务中面对的挑战，并通过在有标注和无标注数据上的学习来提高模型性能和优化处理火星数据的效果。