Data returned from space missions has grown immensely in the last decade. Our goal is to understand the composition and evolution of planetary surfaces. We plan to extract the underlying information from this huge data set using unsupervsed classification spectral data, chemical composition, and ages from the surface of Mercury.

On the one hand, we would like to have an assessment if current remote sensing data from NASA/MESSENGER could resolve different surface regions of Mercury an ideas on the potential of upcoming ESA/BepiColombo mission for improvement. The ultimate goal would be to have a classifciation of Mercury's surface regions with uncertainty assessments, matching with laboratory measurements.

The sheer amount of data returned by scientific missions aimed at exploring the solar system and observing exoplanets in recent decades overwhelms classical methods to explore and discover important scientific aspects of the target body.
As an example, the Mercury data return for Mariner 10 was less than 100 MB, while MESSENGER delivered about 23 TB. Future missions are expected to exceed this limit.
In addition, there is a trend of increasing complexity in the data itself, e.g., going from the of Mariner-10 to the hyperspectral datasets expected from BepiColombo. This situation clearly indicates that some form of automated analysis would be beneficial, provided it is able to save time without a loss of the information content of the data.
Keeping the focus on hyperspectral remote sensing data, the typical approach for analysing this kind of data is to model the observed radiation with a forward radiative model (like Hapke, as in Hamilton et al., 2005) or attempt to reproduce the observed radiation by setting up relevant samples in a laboratory setting using chemical and/or geomorphological context information (e.g., Helbert et al., 2013).
Complex forward models that are able to take into account the relevant physics are typically computationally intensive and difficult to use to investigate the very large parameter space covered by hyperspectral data.
This consideration is even more relevant for laboratory investigations : physical simulation needs the target to be physically fabricated, hence more and and more parameters means more experiments and more time.
Models need computational power to be calculated in a reasonable amount of time, but could be distributed on several machines to overcome this limitation. This workaround is not effective for laboratory experiment, because most only few places meets of the environment needed for space sample simulation, like high-vacuum, -temperature, -radiation and so on. Without a way to efficiently and rapidly explore large amounts of complex data, it is likely that valuable information will be missed in large hyperspectral data sets. Geological maps are the gold standard for remote planetary surface studies, but producing them is an extremely time-consuming task.
This process can suffer from user bias and typically only uses a few data points (e.g., 3-channel images) to describe different units. For example, Denevi et al. (2009) mapped the distribution and extent of major terrain types of Mercury using MESSENGER Mercury Dual Imaging System (MDIS) camera observations of Mercury. While the camera has 11 spectral bands, the maps typically used for the terrain differentiation are RGB, where 3 representative spectral bands are mapped onto the three image color channels.
Geomorphological maps take in account additional features like surface roughness and crater density as a proxy for the age, where the correlation between age and crater density are derived from models (e.g., Bland, 2003; Kerr, 2006).
Automated techniques are becoming more common in planetary science applications, as this books testifies, and the aim of this chapter is to illustrate how to apply unsupervised learning techniques to remote sensing data.
This approach requires minimal user interaction and yields scientifically interesting products like classification maps that can be directly compared with geomorphological maps and models.
We show an analysis of spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 (McClintock and Lankton, 2007).
MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.

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