1. Application of Machine Vision Technology to Martian Geology Ruye WangHarvey Mudd College James Dohm University of Arizona Rebecca CastanoJet Propulsion Laboratory AISRP 2004-2007 April 4, 2005

2. Objectives ► Develop an intelligent system for robust detection and accurate classification in multispectral remote sensing image data ► Demonstrate system in context of Martian geology application

3. Approach ► Pre-conditioning  Modified PCA  Decorrelation Stretch  Conversion to emissivity ► Unsupervised  Kohonen competitive networks  K-Means Euclidean Distance Euclidean Distance Spectral Angular Mapping (SAM) Spectral Angular Mapping (SAM)  Independent Component Analysis (ICA) ► Supervise  Support Vector Machine (SVM)  Other statistical and neural network methods

4. Application to Martian geology ► ► Two regions selected for focused study   Thaumasia highlands   Coprates Rise mountain range

5. Study Motivation ► ► The Wind River Range and the two Martian mountain ranges display similar features such as magnetic signatures, thrust faults, complex rift systems, and cuestas and hogbacks.   Field-based mapping indicates that the Wind River Range records a Late Archean history of plutonism that extends for more than 250 m.y. The range is dominated by granitic plutons, including gneiss, batholith, and granites.   Martian mountain ranges are ancient based on their magnetic signatures. What about their compositions? The detection of mountain-building rocks would provide critical clues to the evolution of the core, mantle, and crust on Mars.

6. Study Objectives/Rationale ► ► Use new tools to investigate the hypothesized diversity of rocks and minerals in the selected regions   Compare to previously reported compositions   Identify materials of low abundance that previous techniques may not have been sensitive enough to identify ► ► Compare the selected Martian regions to the Wind River Mountain Range in Wyoming   Identify if the mountain ranges under investigation contain mountain-building rock materials such as metamorphic and silicic-rich plutonic rocks as identified in the Wind River Mountain Range

7. Comparison of Martian and proposed Earth Analog Sites Coprates Rise mountain range Mars Wind River Mountain Range Wyoming Cuestas and hogbacks, which are caused by tectonic tilting and differential erosion, are visible at both sites

8. Martian Multispectral Data ► ► THermal EMission Imaging System (THEMIS)   on Mars 2001 Odyssey orbiter spacecraft   Low spectral resolution (multi-spectral): ► ► 10 IR channels (6.78-14.88 microns) ► ► 5 VIS channels   High spatial resolution: ► ► IR 100m/pix, VIS 18m/pix ► ► Thermal Emission Spectrometer (TES)   on Mars Global Surveyor spacecraft   High spectral resolution (hyper-spectral): ► ► 143 or 286 channels (6.25-50 microns)   Low spatial resolution: ► ► 3000m/pix

9. Site Selection Themis Image of Thaumasia Highlands USGS Geological Map (based on Viking image)

10. K-Means Clustering with SAM Distance ► ► Shades/shadows in rugged mountain areas do not reflect spectral properties; ► ► Use spectral angles mapping distance (SAM):

11. Competitive Learning Clustering with Normalized Vectors ► ► Normalize both weight and data vectors to consider angular difference only

12. Clustering Results ► ► K-means (SAM, Euclidean) ► ► Competitive net (Kohonen)

13. Comparison of Spectral Angular Map and Euclidean Distance

14. Modified PCA and Decorrelated Stretch

15. Original Themis image First three PCAs Decorrelated Stretch

16. Support Vector Machine (SVM) ► Linear separation

17. Support Vector Machine (cont) Non-linear separation by kernel mapping

18. Support Vector Machine Demo

19. Support Vector Machine Example From left to right:  Training  Results  Themis image  Context

20. Future Work ► Exploration of TES data ► Conversion from radian data to emissivity ► Application of independent component analysis (ICA) ► Usage of spectral library data for supervised training

21. Comparison between THEMIS and TES ► ► THermal EMission Imaging System (THEMIS)   Low spectral resolution (multi-spectral): ► ► 10 IR channels (6.78-14.88 microns) ► ► 5 VIS channels   High spatial resolution: ► ► IR 100m/pix, VIS 18m/pix ► ► Thermal Emission Spectrometer (TES)   High spectral resolution (hyper-spectral): ► ► 143 or 286 channels (6.25-50 microns)   Low spatial resolution: ► ► 3000m/pix

22. Independent Component Analysis (ICA) ► ► In low-spatial resolution image, the spectrum of a pixel may be linear mixture of multiple end- members.   If spectra of end-members are known, least squares methods are used to separate them. [M. Ramsey et al 1998]   Otherwise this is a blind source separation problem, which may be addressed by ICA algorithms.

23. Independent Component Analysis (cont) ► ► Given m linear mixtures (pixels) of n end- members: ► ► Estimate abundances and spectral signatures for the end-members.

24. Independent Component Analysis (cont) ► Given ► Given m linear mixtures (pixels) of n end- members: ► Estimate abundances and end-member spectral signatures or

25. Obtain Temperature and Emissivity from Radian data [e.g., A. Gillespie et al 1998, S. Liang, 2001]

26. Backup Slides

27. Thaumasia highlands and Coprates rise mountain ranges record magnetic signatures, thrust faults, complex rift systems, and cuestas and hogbacks [Dohm et al., 2001], possibly indicative of a plate tectonic phase during extremely ancient Mars [Baker et al., 2002]

28. 3-D oblique view using MOLA data looking to the west across Valles Marineris (C) and the Thaumasia plateau (white ine). Also shown are the locations of the Thaumasia highlands (A) and Coprates rise (B) mountain ranges with respect to Valles Marineris (C), Syria Planum (D), and Tharsis Montes (E). The mountain ranges are ancient as observed in the MGS-based magnet data (Acuna et al., 1999) and structural mapping of Dohm et al., 2001a).

29. Left. MOLA topographic map showing the west-central part of the Thaumasia highlands mountain range, which includes thrust faults (T), complex rift systems (R), shield volcanoes (s), fault systems such as Claritas Fossae (CF), and locales such as Warrego Rise (WR) interpreted to be centers of magmatic-driven uplift and associated volcanism, tectonism, and hydrothermal activity (Anderson et al., 2001). Warrego Rise forms the highest reach within the mountain range. Right. 3-D topographic projection merged with layers of paleotectonic and paleoerosional information of the Warrego Valles source region (Dohm et al., 2001)

30. Detailed geologic map of the northeast part of the Thaumasia region (Dohm et al., 2001). Geologic map units (colored polygons), faults (yellow lines), and ridges (black lines) are shown.