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.

中文翻译：

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CRISM 高光谱数据的 Noise2Noise 去噪

紧凑型火星勘测成像光谱仪 (CRISM) 获取的高光谱数据可以对火星表面矿物学进行无与伦比的测绘。由于传感器随着时间的推移而退化，最近获取的数据的很大一部分被认为无法使用。这里引入了一种新的数据驱动模型架构Noise2Noise4Mars (N2N4M)来消除CRISM图像中的噪声。我们的模型是自我监督的，不需要零噪声目标数据，使其非常适合在高质量标记数据稀缺的行星科学应用中使用。我们展示了它在合成噪声数据和 CRISM 图像上的强大性能，及其对下游分类性能的影响，在大多数指标上都优于基准方法。这样可以对火星表面的关键感兴趣地点进行详细分析，包括拟议的着陆器地点。