GEOG 482/582: Satellite Image Classification &
Change Detection
(CRN: 61432, 4 credits)
Spring 2012
2012
TERM PROJECT REQUIREMENTS
Tue, Thu 16:00 to 17:50pm CH 418
(Tuesdays) and CH 469 (Thursdays)
Course webpage: http://web.pdx.edu/~nauna/
Instructor: Lynnae Sutton (email: mailto:nauna@pdx.edu)
Office: Cramer Hall Geography Office Phone:
(503)720-8205
Office hours: Tue, Thu after class or by appointment
Course Objectives
This course focuses on advanced
satellite image classification methods that can be used for thematic
information extraction as well as digital change detection methods for
measuring land use/ land cover change. The course includes computer
exercises in advanced classification methods (e.g., Fuzzy and decision tree
classification), radiometric normalization, and change detection using leading
satellite image processing software packages including ERDAS Imagine and IDRISI
TIAGA.
Readings
The course readings are a series of
papers that will be distributed by the instructor as well as digital documents.
The course will be taught in a seminar format. Each student will pick and read
several journal papers and be a discussion leader to review and criticize these
papers.
Grading
Attendance
to this course is mandatory. If you miss class you are required to show me the
work you have completed, including a written synopsis of the reading you have
completed. PLEASE DO NOT MISS CLASS. You are expected to take part in the
discussions (THE DISCUSSION PARTICIPATION IS PART OF YOUR GRADE AND IT CAN ONLY
HAPPEN IN CLASS).
Discussion/ Article
Critique- Synopsis (40%)
The course will be taught in a seminar
format, which means that students are not passive members of the class.
Students are expected to actively contribute to each class period. To
facilitate an interactive discussion, students will lead journal article
discussion during the semester for which they will receive a grade. The
discussion leaders must do three things. First, they must thoroughly read
the reading and write a 1-2 page critique/synopsis. The synopsis
part should highlight the main points of the reading and the critique part should
identify strengths and weaknesses of the reading. Second, they should develop
4 discussion questions. These questions, as well as the
critique/synopsis, should be typed with answers and given to the instructor THE
THURSDAY PRIOR TO THEIR SCHEDULED IN CLASS DISCUSSION. Third, they are
responsible for leading the classroom discussion along with the instructor. The discussion schedule
will be handed out in class and posted as a link on the top of this webpage.
It is important that everyone in the class take part in these discussions.
Therefore, class attendance and participation are mandatory because this is 40%
of your grade.
You will conduct labs that will help
you learn the methods necessary to do a project. The practical exercises
provide a way to acquire skills using ERDAS Imagine and IDRISI and to apply the
course concepts to real data. The lab manual (ERDAS Tour Guide) and ERDAS Field
Guide are available in Acrobat pdf format on the I drive.
Project
Presentation and Paper (30%)
A satellite remote sensing project
is required for all students. The
project is intended to provide a deeper understanding of image classification
and/or change detection through experience. You must submit an outline of your
project in the 4th week and present the project during a scheduled
time at the end of the term. Every project presentation and paper must include
the following sections: an Introduction, Data, Methods, Results, and
Conclusions. 2012 TERM PROJECT REQUIREMENTS
|
Week |
Tuesday
(Lecture/Seminar) |
Thursday
(Lab/Project) |
|
1 Apr
3/6 |
Course Overview & Review of
Vegetation Indices (Slides) Review basic remote sensing
reading - Jensen Chapters 1 through 7 or similar material from books from
previous courses |
Lab 1. Selection
of article (10 points) Lecture on Knowledge-based
classification (Slides) |
|
2 Apr
10/12 |
Remote Sensing Applications (NASA
Online Tutorial Section 3: vegetation Applications) readings to
be done for class Vegetation Indices Contd. (slides) Change Detection (Readings Mas 1999 and Lu
et al. 2004) |
Continue Lab 2. Knowledge-based classification (20 points) |
|
3 Apr
17/19 |
Change Detection (Readings Mas 1999 and Lu
et al. 2004) (Slides)
|
Lab 2. |
|
4 Apr
24/26 |
Lecture – Change Detection Summary
(slides), Radiometric
normalization (slides) LAB 2 DUE (15pts) |
Complete Lab 2. |
|
5 May
1/3 |
Lecture –
Change Vector Analysis,
Soft classifiers (slides) /
Journal Article Discussion |
Lab 3.
Radiometric normalization data (Lab3_ASCR Locture – slides) (15 points)
|
|
6 May
8/10 |
Lecture (Slides) Advanced Classifiers / Journal Article Discussion Fehrenbach, Erik: (Yue)
The relationship between land surface temperature and NDVI with remote
sensing: application to Shanghai Landsat
7ETM+ data |
Lab 4. Change vector analysis (20
points) LAB 3 DUE (15pts),
PROJECT TOPIC |
|
7 May
15/17 |
Blackmore, Debbie:
Wynne, Timothy T., Stumpf, Richard P.,
Tomlinson, Michelle C, Ransibrahmanakul, Varis, Villareal, Tracy A.
2005. Detecting Karenia
brevis blooms and algal resuspension
in the Western Gulf of Mexico with satellite ocean color imagery.
(Wynne_etal_2005.pdf) |
Lab 4. Change
vector analysis (20 points) data Start Lab 5.
Advanced classifier (15
points) |
|
8 May
22/24 |
Lecture- Artificial neural network
(slides) / Journal Article Discussion Meenakshi
Rao: Dewan & Yamaguchi (2008), Using remote sensing and GIS to
detect and monitor land use and land cover change in Dhaka. |
Complete Lab 5 Start Lab 6. Idrisi Advanced classifiers (10 points extra credit)
|
|
9 May
29/31 |
1.
Review
of Digital Image Analysis (Slides) 2.
Sample haze
reduction and topographic normalization lab without data. 3.
Another
sample topographic normalization lab without data. 4.
ERDAS Tour Guide
(supervised classification tutorial on pages 479-510 and accuracy assessment
pages 521-532). For other labs see labs 1-7 for geog481 at the top of this
document. If you need data please let
me know and I will link it here. 5. Knowledge base article on cloud reduction
technique. Song et al 2002 6. Texture Analysis article, Gluch
2002 7. Texture Analysis article, Zhang 2001 8.
Land Cover Mapping, comparison between
manual digitizing and automated classification of black and white historical
aerial photography, Awwad,
2003, thesis Unv of Florida (has a good section
on texture analysis) 9.
Digital image
texture information within ArcGis 10. Merging
Landsat and DOQ within ArcGis 11.
MORE
RESOURCES |
Continue Lab 6. Idrisi Advanced classifiers (10 points extra credit)
Project work PROJECT DATA: |
|
10 Jun
5/7 |
|
Project work |
|
Finals
week Jun
11/16 |
Finish Lab work |
Project Oral Presentations |