GEOG 416: Satellite Image Analysis
GEOG 579: Remote Sensing
Spring Semester 2009
4:30pm-7:10pm Monday, Robinson Hall B106
Instructor:
Dr. Wenli Yang
Email: wyang1@gmu.edu
Phone: 301-614-5312,
703-993-9236
327 Research I
Office Hours: 11:00-12:00,
1:00-4:00pm, Monday, or by appointment
Course description:
This is an intermediate remote sensing course with a focus on digital processing of Earth observing satellite imagery. Students will be introduced to physical principle of remote sensing, Earth observing satellite systems and sensors, and various digital processing techniques related to remote sensing imagery. Topics of the course will include solar radiation, remote sensing data collection systems, image quality assessment, radiometric and geometric corrections, image enhancement, transformation, classification, and change detection.
Text book:
Introductory
Digital Image Processing: a Remote Sensing Perspective, 3rd edition
By John R. Jensen,
Pearson Prentice Hall, 2005
Prerequisites:
GEOG 412 and college
algebra and statistics; or permission of instructor.
Class schedule:
Jan. 26
Course description
Introduction to
electromagnetic radiation (Chapter 6, pp175-194)
Introduction to
Remote Sensing (Chapter 1)
Feb. 2
Introduction to
Remote Sensing (cont. Chapter 1)
Remote sensing data
collection (Chapter 2)
Feb. 9
Remote sensing data
collection (cont. Chapter 2)
Digital Image
Processing Functions and Software (Chapter 3)
Exercise
#1 handed out.
Feb. 16
Image statistics (Chapter
4)
Display and
Visualization (Chapter 5)
Feb. 23
Radiometric
correction (Chapter 6, pp.194-222)
Geometric
correction (Chapter 7)
Exercise
#1 due. Exercise #2 handed out.
Mar. 2
Geometric correction
(cont. Chapter 7)
Image enhancement (Chapter 8)
Mar. 9 Spring break
Mar. 16
Image enhancement
(cont. Chapter 8)
Exercise
#2 due. Exercise #3 handed out.
Mar. 23
Image classification (Chapter 9)
Midterm Examination (the last 90 minutes of the class, 5:40-7:10pm)
Mar. 30 Image
classification (cont. Chapter 9)
Apr. 6 Image classification
(cont. Chapter 9)
Exercise
#3 due. Exercise #4 handed out.
Apr. 13 Artificial intelligence (Chapter 10)
Apr. 20 Change detection (Chapter 12)
Exercise
#4 due. Exercise
#5 handed out.
Apr. 27 Thematic
map accuracy assessment (Chapter 13).
Hyperspectral image analysis (Chapter 11)
May. 4 Hyperspectral image analysis (cont. Chapter 11)
Exercise
#5 due.
Course
project due (for GEOG579 only).
May 11 Final Examination 4:30-7:15pm
May 14 Grades
reported to the registrar office
Exercises:
All exercises will
involve the use of the Erdas/Imagine software available in the GIS/remote sensing
lab of the Geography department (http://geoglab.gmu.edu). Each exercise must include a brief report
that describes goal, methods, results, discussion, and
summary/conclusions. Each report should
be at least one single space page long (12-point font, 1-inch margins), plus
necessary graphics/maps/tables.
Exercises are due at the class beginning time of the due dates listed in
the class schedule. Late submissions
will be penalized at 2-point for each day they are submitted late.
#1: basic image
manipulations using Erdas/Imagine
#2: radiometric and
geometric correction
#3: image enhancement
#4: image classification
#5: change detection
Course project (for GEOG 579 only)
For graduate students enrolled in GEOG 579, a land cover classification
and change detection project is required.
The main classification and change detection procudes are similar to
those used in exercises #4 and #5.
However, images used in these two exercises are not allowed to be reused
in the course project. Students shall
identify the study area and two dates between which the change detection will
be performed and shall obtain Landsat TM or ASTER images for the select
dates (TM and ASTER images are freely obtainable). Each student needs to design his/her own classification scheme
suitable for the selected area and dates.
A short paper shall be submitted together with the classification and
change detection results. The paper
should describe the goal, study
area, classification scheme, classification methods, and results. It should also include analysis, discussion,
and summary/conclusions. The paper should
be double spaced, in 12-point Times font, with 1-inch margins, and at least
10-page long, not including images/graphics.
Grading policy:
Exercises: 50 points (10 pts each)
Midterm exam: 20 points
Final exam: 30 points
Total points: 100
A+: 97-100 A: 93-96 A-: 89-92
B+: 85-88 B: 81-84 B-:
77-80
C+: 73-76 C: 69-72 C-: 65-68
D: 60-64 F: 0-59
Exercises: 50 points (10 pts each)
Course project: 50 points
Midterm exam: 20 points
Final exam: 30 points
Total points: 150
A+: 145-150 A: 139-144 A-:
133-138
B+: 127-132 B: 121-126 B-:
115-120
C: 91-114 F: 0- 90