用神经网络自动识别火星表面土丘，助力探测水和生命迹象
To GAN or Not To GAN: Segmentation Analysis on Mars DEM
- 采用监督语义分割与生成对抗网络自动检测火星土丘
- 生成数据未提升性能，真实标注数据更有效
- 适合行星科学与遥感图像分析研究者
为更好理解火星表面以支持探测车自主导航，需准确识别土丘位置。这些地貌的探测有助于寻找地外生命证据，特别是水或宜居环境线索。以往通过人工在数字高程模型上标注形态参数完成检测。本文提出基于神经网络的语义分割方法，实现火星土丘的自动检测与预测。采用监督语义分割模型与生成对抗网络进行对比实验，结果表明：引入额外人工生成数据并未提升性能，真实标注数据在该任务中更具价值。
原文摘要 · Abstract (English)
To better understand Martian Surface, which is needed to enable Rovers navigate Mars with ease, it is necessary to be able to determine the location of mounds. Detecting and studying these morphologies can also help us find evidence of extraterrestrial life, in this case, more specifically, water or signs of life conducive environments. Detection of mounds was done by manually mapping morphological parameters onto Digital Elevation Models. This paper solves the problem by automatically detecting and or predicting mounds on Mars using Neural Network based Semantic Segmentation methodologies. This is done by using supervised semantic segmentation model and generative adversarial approach. A comparison of the approaches shows that adding extra artificially generated data did not improve the result.
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