Effective classification for crater detection: A case study on Mars

Wang, Jue; Ding, Wei; Fradkin, Barry; Pham, Cuong Hung; Sherman, Peter; Tran, Binh D.; Wang, Dawei; Yang, Yun; Stepinski, T. F.

doi:10.1109/coginf.2010.5599824

Cited by 3 publications

(1 citation statement)

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“…Many studies have focused explicitly on the relationship between these two bands (e.g., [67][68][69][70]), and developing indices for interpreting spectral and land information space. A few studies have also applied algorithmic classification techniques (both supervised and unsupervised) to Martian datasets, including: hyperspectral imagery and mineralogy data from orbital measurements [71][72][73][74][75][76][77] and ground-measurements by the rovers [78]; terrain mapping and feature classification from elevation and surface roughness data [79][80][81][82] and visual imagery [83]; and automated detection of impact craters [84,85]. The use of algorithmic classification in studies of Mars is increasing over time, however no previous study has applied algorithmic classification to mapping surface grain size and thermal behaviour in Martian thermal inertia and albedo data.…”

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confidence: 99%

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

et al. 2014

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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.

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