1Emanuele Plebani,2Bethany L.Ehlmann,2,3Ellen K.Leask,4Valerie K.Fox,1M. Murat Dundar
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2021.114849]
1Computer and Information Sciences Department, Indiana University – Purdue University, Indianapolis, 46202, IN, USA
2Div. of Geological & Planetary Sciences, California Institute of Technology, Pasadena, 91125, CA, USA
3John Hopkins University Applied Physics Laboratory, Laurel, 20723, MD, USA
4Department of Earth and Environmental Sciences, University of Minnesota, Minneapolis, 55455, MN, USA
Hyperspectral images collected by remote sensing have played a significant role in the discovery of aqueous alteration minerals, which in turn have important implications for our understanding of the changing habitability on Mars. Traditional spectral analyses based on summary parameters have been helpful in converting hyperspectral cubes into readily visualizable three channel maps highlighting high-level mineral composition of the Martian terrain. These maps have been used as a starting point in the search for specific mineral phases in images. Although the amount of labor needed to verify the presence of a mineral phase in an image is quite limited for phases that emerge with high abundance, manual processing becomes laborious when the task involves determining the spatial extent of detected phases or identifying small outcrops of secondary phases that appear in only a few pixels within an image. Thanks to extensive use of remote sensing data and rover expeditions, significant domain knowledge has accumulated over the years about mineral composition of several regions of interest on Mars, which allow us to collect reliable labeled data required to train machine learning algorithms. In this study we demonstrate the utility of machine learning in two essential tasks for hyperspectral data analysis: nonlinear noise removal and mineral classification. We develop a simple yet effective hierarchical Bayesian model for estimating distributions of spectral patterns and extensively validate this model for mineral classification on several test images. Our results demonstrate that machine learning can be highly effective in exposing tiny outcrops of specific phases in orbital data that are not uncovered by traditional spectral analysis. We package implemented scripts, documentation illustrating use cases, and pixel-scale training data collected from dozens of well-characterized images into a new toolkit. We hope that this new toolkit will provide advanced and effective processing tools and improve community’s ability to map compositional units in remote sensing data quickly, accurately, and at scale.