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Remote Sens. 2014, 6(6), 5184-5237; doi:10.3390/rs6065184

An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

1
Research School of Astronomy and Astrophysics, Planetary Sciences Institute, Mount Stromlo Observatory, Australian National University, Cotter Road, Weston, ACT 2611, Australia
2
The Fenner School of Environment and Society, Building 48, Australian National University, Canberra, ACT 0200, Australia
3
Centre for Planetary Science and Exploration, University of Western Ontario, London, ON N6A 5B7, Canada
4
Department of Earth Sciences, University of Western Ontario, London, ON N6A 5B7, Canada
5
Mars Society Australia, Canberra, ACT 2606, Australia
6
Division of IT, Engineering and the Environment, Bonython Jubilee Building, University of South Australia, GPO Box 2471, Adelaide, SA 5001, Australia
7
Research School of Physics and Engineering, Mills Road, Australian National University, Canberra, ACT 0200, Australia
8
Space Science Institute, Boulder, CO 80301, USA
*
Author to whom correspondence should be addressed.
Received: 16 October 2013 / Revised: 12 May 2014 / Accepted: 13 May 2014 / Published: 5 June 2014

Abstract

Thermal inertia and albedo provide information on the distribution of surface materials on Mars. These parameters have been mapped globally on Mars by the Thermal Emission Spectrometer (TES) onboard the Mars Global Surveyor. Two-dimensional clusters of thermal inertia and albedo reflect the thermophysical attributes of the dominant materials on the surface. In this paper three automated, non-deterministic, algorithmic classification methods are employed for defining thermophysical units: Expectation Maximisation of a Gaussian Mixture Model; Iterative Self-Organizing Data Analysis Technique (ISODATA); and Maximum Likelihood. We analyse the behaviour of the thermophysical classes resulting from the three classifiers, operating on the 2007 TES thermal inertia and albedo datasets. Producing a rigorous mapping of thermophysical classes at ~3 km/pixel resolution remains important for constraining the geologic processes that have shaped the Martian surface on a regional scale, and for choosing appropriate landing sites. The results from applying these algorithms are compared to geologic maps, surface data from lander missions, features derived from imaging, and previous classifications of thermophysical units which utilized manual (and potentially more time consuming) classification methods. These comparisons comprise data suitable for validation of our classifications. Our work shows that a combination of the algorithms—ISODATA and Maximum Likelihood—optimises the sensitivity to the underlying dataspace, and that new information on Martian surface materials can be obtained by using these methods. We demonstrate that the algorithms used here can be applied to define a finer partitioning of albedo and thermal inertia for a more detailed mapping of surface materials, grain sizes and thermal behaviour of the Martian surface and shallow subsurface, at the ~3 km scale. View Full-Text
Keywords: algorithmic classification; Gaussian Mixture Model; ISODATA; Maximum Likelihood; albedo; thermal inertia; Mars; grain size algorithmic classification; Gaussian Mixture Model; ISODATA; Maximum Likelihood; albedo; thermal inertia; Mars; grain size
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Jones, E.; Caprarelli, G.; Mills, F.P.; Doran, B.; Clarke, J. An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques. Remote Sens. 2014, 6, 5184-5237.

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