  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.
