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

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 OPEN ACCESS Remote Sens. 2014, 6 5185 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.

Global documentation of gullies with the Mars Reconnaissance Orbiter Context Camera and implications for their formation

“…hemispheres were typically found in areas of higher albedo and lower thermal inertia relative to global trends. The Heldmann and Mellon (2004) (Cantor et al, 2002;Smith, 2004;Tamppari et al, 2008); therefore, MY24 albedo values should be the most representative of average martian surface materials (Jones et al, 2014). The different datasets used may thus account for the differences in our observations vs. those of Heldmann and Mellon (2004) and Heldmann et al (2007).…”

“…However, the variability in relative gully ages as compared to dunes suggests that optimal conditions for gully formation have not remained constant over time in individual locations. Jones et al (2014) conducted global thermophysical mapping of Mars using combined albedo and nighttime thermal inertia values derived from the Mars Global Surveyor Thermal Emission Spectrometer (TES), refining the previous mapping of Putzig et al (2005). They defined 7 classes of surface materials, distinguished by their dominant grain size, degree of induration, and albedo (correlated with mineralogy).…”

Selection of the InSight Landing Site

“…The VL, Spirit, PHX and MSL landing sites sample the moderate thermal inertia and intermediate to high albedo unit C (Fig. 13, Table 7) that is dominated by crusty, cloddy, blocky or frozen soils (duricrust) with various abundances of rocks and bright dust (Golombek et al 2008a;Jakosky and Christensen 1986;Christensen and Moore 1992;Moore and Jakosky 1989;Mellon et al 2000Putzig et al 2005;Jones et al 2014). The Opportunity landing site is in the moderate thermal inertia and low albedo surface unit B that is relatively dust free and composed of dark eolian sand and/or increased abundance of rocks.…”

Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water

“…The iterative self-organizing data analysis technique (ISODATA) is a widely used classification methods (Micallef et al, 2007) that approximate the natural construction of a multidimensional dataset by iteratively passing it through defining classes to minimize pixel separation distance (D) and sum of squared error (SSE) (Ball and Hall, 1967;Jones et al, 2014). …”