Application of Machine Vision Technology to Martian Geology

1
Application of Machine VisionTechnology to
Martian Geology

• Ruye Wang Harvey Mudd College
• James Dohm University of Arizona
• Rebecca Castano Jet 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
• 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
Modified PCA and Decorrelated Stretch
Original Themis image
First three PCAs
Decorrelated Stretch
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Support Vector Machine (SVM)

• Linear separation

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Support Vector Machine (cont)

• Non-linear separation by kernel mapping

18
Support Vector Machine Demo
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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.

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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 m linear mixtures (pixels) of n end-members

or

• Estimate abundances and end-member
  spectral signatures

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