Subject: GEOS 622, Image processing (3 CR)
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Date: Wed, 11 Sep 2013 13:36:57 -0800

GEOS 622 Overview

Images

Schedule & Notes

GEOS 622, Digital image processing in the
geosciences (3 CR)

Time & place: Tuesdays & Thursdays, 9.00-10.30am, Image-analysis teaching
lab, Level 0 West Ridge Res. Bldg. (WRRB 004)
Instructor: Hajo Eicken, Geophysical Institute,
UAF
e-mail: [email-masked]
Phone: 474-7280
Office: WRRB 104E (office hours:
Wednesdays 11.00am –12.30pm - in WRRB 004 as needed - and by
appointment)

Class outline:
This class covers both the theory of key
image-processing approaches and methods as well as hands-on exercises and
analyses in the UAF Remote Sensing Image Processing Lab (where all classes will
be taught in front of computer work stations). While the focus of the class is
on the acquisition, processing and analysis of remote-sensing data and imagery
in the geosciences, the methods taught are applicable to a wider range of
fields, such as microscopy in the geo- and biosciences or image analysis in
engineering applications.
The class is divided into six parts of roughly
equal weight, starting with an overview of the fundamentals of image acquisition
and processing. This is followed by sections addressing the three main
methodologies of processing and analyzing single-channel (grey-shade) images.
With increasing degrees of complexity these are histogram or point-based
operations (considering only the summary statistics of sets of pixels in an
image), spatial transforms (taking into consideration the arrangement of pixels
in the image), and transforms in the frequency domain (representing and
analyzing an image as a superposition of regular, harmonic patterns). An
understanding of these principal approaches then allows for an in-depth
examination of different approaches to pattern recognition and image
segmentation. The final part of the class then considers image processing and
analysis of multi-spectral images.

A course schedule can be found here.

Expected outcomes:
Successful completion of the course
will allow students to
• Assess how image processing and image analysis may
help solve a specific problem in the geosciences or related disciplines;
•
Identify the appropriate selection of imaging approaches or imagery pertinent to
the problem;
• Develop an approach and workflow that builds on the concept of
the image-processing chain to move from raw imagery or geospatial data to a
quantitative representation and digest of the information contained in the
imagery;
• Use their acquired proficiency in digital image-processing to
apply the appropriate tools out of the major image-processing methodologies to
help solve geoscience problems;
• Synthesize quantitative information
obtained through image processing into a report or product that addresses a
specific question or problem:
• Communicate the results of such work
effectively in oral and written form.

Grading policy: Grades will be based on the mid-term
and final exam (for a combined total of 20%), a class project (30%), involving a
presentation and a report, four sets of homework (40%), and class participation
(10%), which is based on attendance and contribution to discussion in
class.
The following letter grade system will be used: A+ for better
than 95% performance (number of total possible points) summed over all
categories, A >90 to 95%, A- >85 to 90%, B+ >80 to 85%, B >75 to
80%, B- >70 to 75%, C+ >67 to 70%, C >63 to 67%, C- >60 to 63%, D 50
to 60%, F <50%.

Term project:
A term project, to be completed by
the end of the semester, will be aimed at applying skills and expertise acquired
during the course to a specific scientific or engineering problem. Students are
highly encouraged to define a project of their own (e.g., originating from
thesis-related research), but a number of project suggestions (incl. data,
samples etc.) will also be offered by the instructor.

Image processing program resources:
• IDL and Envi
as available through licenses for teaching lab
• ImageJ plus spin-offs are
available for project work (program and documentation available at http://rsb.info.nih.gov/ij)
• access to
other image-processing programs as available

Lab access:
Students in the class will have lab access
outside of class hours to work on projects or homework. For a schedule of lab
hours and potential conflicts with other activities in the WRRB 004 lab, please
visit http://lab.gina.alaska.edu/
and view the Lab Calendar.

Selection of textbooks and primary
references:

*Castleman K. R. (1996) Digital image processing,
Prentice Hall, Upper Saddle River, NJ. (thorough treatment, extensive coverage
of frequency-domain transforms and pattern recognition)

*Gonzalez R. C., and R. E. Woods (2007) Digital image
processing, 3rd ed., Prentice Hall, Englewood Cliffs, NJ (the 1987 edition
of Gonzalez and Wirtz is also still useful),

*Jähne B. (2005) Digital image processing - Concepts,
algorithms, and scientific applications, 6th ed., Springer-Verlag, Berlin.
(thorough treatment, extensive coverage of motion and space-time image
analysis)

*Jain A. K. (1989) Fundamentals of digital image
processing, Prentice Hall, Englewood Cliffs, NJ. (thorough treatment
focussing on signal processing, with particular coverage of image filtering,
restoration and representation)

*Jong, S. M. de (2006) Remote sensing image analysis: Including the
spatial domain, Kluwer Academic, Dordrecht.

*Ohser, J., K. Schladitz (2009) 3D images of materials structures:
Processing and analysis. Wiley-VCH. 9 (excellent introduction to key
techniques relevant for microstructural analysis)

*Rosenfeld A., and A. C. Kak (1982) Digital picture
processing, vols. 1 and 2, Academic Press, Orlando. (thorough, classic
text)

*Russ J. C. (2011) The image processing handbook, 6th
ed., CRC Press, Boca Raton, FL. (mostly non-mathematical, visually
oriented text with numerous examples grouped into problem areas)

*Schowengerdt R. A. (2006) Remote sensing - Models and
methods for image processing, 3rd ed., Academic Press, San Diego. (solid,
thorough text for remote-sensing aspects, with very good coverage of spectral
transforms and classification)

Serra J. P. (1982) Image analysis and mathematical
morphology, Academic Press, London. (classic text, a must for the
non-linear filtering aficionado, not necessarily required reading for the
casual passer-by; summary of concepts of mathematical morphology can be found
in more recent textbooks, e.g., Castleman, 1996, or Russ, 2011, for numerous
examples)

* books marked with an asterisk (may not be most recent
edition, however) will be put on hold for the duration of the semester in the
Keith Mather Library (Geophysical Institute & IARC); others are available at
Rasmuson Library or with the instructor

N.B.: Comments in parentheses are highly subjective and
merely meant to provide some guidance as to the contents and depth; prices
quoted are approximate.

Special needs. Students with learning or other disabilities who may
need classroom accommodations are encouraged to visit the Disabilities website and make an
appointment with the Office of Disability Services (474-5655). Please meet with
the instructor so that the appropriate accommodations and supports to assist in
meeting the goals of the course can be made in collaboration with the Office of
Disability Services.

Academic integrity. Those enrolled in this class are subject to the Student
Code of Conduct as outlined in University Regents' Policy on Student Rights
and Responsibilities.

Last update: September 3, 2013

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