标题： 青海-西藏高原柴达木盆地火星着陆点类比物的深度学习选择 显示英文标题

标题： Deep learning selection of analogues for Mars landing sites in the Qaidam Basin, Qinghai-Tibet Plateau

摘要： 遥感观测和火星车任务记录了火星沉积物中存在沙滩、盐湖和风蚀地貌。 所有这些观测结果表明，火星在其早期历史中曾有水分。 火星上曾经有海洋，但现在已干涸。 因此，火星上先前生命的迹象可能被在此过程中形成的蒸发岩保存下来。 因此，对蒸发岩区域的研究已成为火星生命探索的优先领域。 本研究提出了一种从地球和火星地表地貌图像数据中训练相似性度量的方法，可用于识别或验证应用。 该方法将用于模拟任务中，使用火星类比地区——青海高原柴达木盆地的蒸发岩区域的小规模区域来选择火星着陆点。 这一学习过程最小化了判别损失函数，使得同一地点的图像相似性度量更小，而不同地点的图像相似性度量更大。 本研究选择了一个基于卷积神经网络（CNN）的模型，该模型已被训练以解释图像外观的各种变化，并在火星中识别不同的地貌。 通过识别不同的地貌，可以选择火星上的优先着陆点。 显示英文摘要

摘要： Remote sensing observations and Mars rover missions have recorded the presence of beaches, salt lakes, and wind erosion landforms in Martian sediments. All these observations indicate that Mars was hydrated in its early history. There used to be oceans on Mars, but they have now dried up. Therefore, signs of previous life on Mars could be preserved in the evaporites formed during this process. The study of evaporite regions has thus become a priority area for Mars' life exploration. This study proposes a method for training similarity metrics from surface land image data of Earth and Mars, which can be used for recognition or validation applications. The method will be applied in simulating tasks to select Mars landing sites using a selecting small-scale area of the Mars analaogue the evaporite region of Qaidam Basin, Qinghai-Tibet Plateau. This learning process minimizes discriminative loss function, which makes the similarity measure smaller for images from the same location and larger for images from different locations. This study selected a Convolutional Neural Networks (CNN) based model, which has been trained to explain various changes in image appearance and identify different landforms in Mars. By identifying different landforms, priority landing sites on Mars can be selected.