GEOG 472 Remote Sensing:
Digital Processing and Analysis
Spring 2008
Lecture: Monday 3:00 - 5:00pm
LeFrak Hall 2166
Labs: Section1：Wednesday 1:00-3:00pm
Section2： Thursday
11:00-1:00pm
LeFrak
Hall 1138
Instructor: Dr. Shunlin Liang
301-405-4556
http://www.glue.umd.edu/~sliang
Office hours: 1-3pm
Monday & by appointment
Lab Instructor: Ms. Wenhui Wang
Office hours: 10-11am Weds. & Thur.
Prerequisites: GEOG 372 or its equivalent; Geog306 or its
equivalent.
Required Textbook:
“Introductory
Digital Image Processing: A Remote Sensing Perspective”, John Jensen, Prentice Hall, third edition, 544pp. 2004;
Instruction
materials for the labs are at
http://www.geog.umd.edu/academic/courses/spring.html
Course Content: The class will
build upon principles introduced in GEOG 372 (Introduction to Remote Sensing),
and emphasize the advanced techniques for extracting land surface information
from remote sensing imagery. It is a highly technical course but will be taught
in a non-quantitative way. The lectures will cover the following themes:
·
background (Lectures 1-2)
·
physically understanding remotely sensed data
(Lectures 3-4)
·
pre-processing techniques(Lectures 5-6 &3)
·
information extraction techniques (Lectures 7-13)
·
application demonstrations (Lecture 14).
Laboratory sessions will give students
hands-on experience in the fundamentals of digital image processing and information
extraction techniques.
Requirements: This course
requires one mid-term exam, one final exam, 11 Lab exercises and one final
project. The final project will be performed by small groups of 3-4 undergraduate
students or individual graduate student. The project topic
will be given in the first class. The student will relate each lecture to their
project and put all pieces together at the end. The grade for each group is
based on the presentation and the written report. The specific requirements
will be discussed in class.
As per University standards, under no
circumstances may students copy the work of others and submit it as their own.
Doing so will be treated as academic dishonesty and treated as such. For more
information on academic dishonesty, attendance, and assessment policies of the
University, please read the Spring Schedule of Classes.
Students with Learning
Disabilities: If you have a documented disability and wish
to discuss academic accommodations, please contact the instructor as soon as
possible.
Grading: Mid-term exam (20%),
Final exam(30%), Labs(30%), final project (20%).
Date Topic
1(Jan.28) Introduction:
a Systematic View of Remote Sensing (Chapter 1)
Lab: Introduction to PCI
2(Feb. 4)
Earth Observation Missions and Instrumentation (Chapter 2)
Lab: Methods for managing data
using the software PCI
3(Feb. 11) Understanding
Surface Signatures (supporting
materials)
Lab: Analyzing and understanding
spectral data
4(Feb. 18) Atmospheric
Effects in Optical Imagery and Correction (Chapter 6)
Lab: Atmospheric correction
5(Feb. 25)
Radiometric Calibration and Preprocessing (supporting
materials)
Lab: Image enhancement
6(Mar. 3)
Geometric Processing (Chapter 7)
Lab: Geometric correction &
image registration
7(Mar. 10) Feature
Extraction (Chapters 8 & 11)
Lab: Principal component analysis
and image transformation
March 17
Spring Break
8(Mar. 24)
Spatial and Temporal Analysis (supporting materials)
Lab: Image composite
9(Mar. 31) Image Classification Techniques
(Chapter 9)
Lab: Clustering
analysis & supervised classification
April 7 Mid-term Exam
10(April 14) Land
Use/Cover Mapping (Chapter 9)
Lab: No LAB due to AAG meeting
11(April 21) Change
Detection (Chapter 12)
Lab:
Change detection
Lab: Biophysical variable
estimation
13(May 5)
Estimation of Surface
Geophysical Variables (supporting materials)
Lab: final project
14(May 12) Application
Demonstrations (supporting materials)
Lab: final project presentation
May 19 Final Exam