Hyperspectral data acquired by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) have allowed for unparalleled mapping of the surface mineralogy of Mars. Due to sensor degradation over time, a significant portion of the recently acquired data is considered unusable. Here a new data-driven model architecture, Noise2Noise4Mars (N2N4M), is introduced to remove noise from CRISM images. Our model is self-supervised and does not require zero-noise target data, making it well suited for use in Planetary Science applications where high quality labelled data is scarce. We demonstrate its strong performance on synthetic-noise data and CRISM images, and its impact on downstream classification performance, outperforming benchmark methods on most metrics. This allows for detailed analysis for critical sites of interest on the Martian surface, including proposed lander sites.

翻译：摘要：由火星勘测轨道飞行器上的紧凑型火星光谱成像仪（CRISM）获取的高光谱数据，为实现火星表面矿物学的高分辨率制图提供了前所未有的支持。然而，由于传感器随时间的退化，近期获取的大量数据被认为已无法使用。本文提出一种新型数据驱动模型架构——Noise2Noise4Mars（N2N4M），用于去除CRISM图像中的噪声。该模型采用自监督方式，无需零噪声目标数据，因此特别适用于高质量标注数据稀缺的行星科学应用场景。我们通过合成噪声数据与CRISM真实图像验证了其强大性能，并评估了该模型对下游分类任务的影响——在大多数评估指标上优于基准方法。这一成果使得对火星表面关键兴趣点（包括拟议着陆点）的精细分析成为可能。