Spring 2020 - GEOG 353 D100

Advanced Remote Sensing (4)

Class Number: 3181

Delivery Method: In Person

Overview

  • Course Times + Location:

    Mo 10:30 AM – 12:20 PM
    RCB 8100, Burnaby

  • Exam Times + Location:

    Apr 24, 2020
    8:30 AM – 11:30 AM
    Location: TBA

  • Prerequisites:

    GEOG 253.

Description

CALENDAR DESCRIPTION:

Advanced remote sensing principles and techniques, including physics-based modeling, advanced classifiers, automated data processing, and integration of ancillary data products. Quantitative.

COURSE DETAILS:

Welcome to Advanced Remote Sensing.  This course expands on the physical principles of remote sensing introduced in GEOG 253, and explores more advanced methods used in contemporary studies of the Earth’s surface.  The focus is on extraction of quantitative information and how to derive it with state-of-the-art image processing techniques, including some automation.  

Learning objectives:  

On successful completion of this course, students will:  
·         Have a stronger understanding of the physical basis of satellite-based remote sensing.
·         Be aware of the wide range of uses of modern remote sensing.
·         Be able to apply a range of advanced image processing techniques to extract information and produce maps from remote sensing data.
·         Calculate diverse indices based on mathematical relationships between spectral bands.
·         Transform remotely sensed data into physically-meaningful variables.
·         Understand hyperspectral remote sensing.
·         Manipulate diverse software packages for remote sensing data processing and visualization.
·         Have access to a diverse and curated list of remote sensing data sources.  

Course organization:  

There will be one 2-hour lecture per week.  Students will learn about and practice remote sensing techniques through formal lectures, student-oriented discussions, computer labs and individual assignments.  2-hour lab sessions will be conducted most weeks (not the first), based on using sample data sets and software analysis to reinforce the theoretical concepts and methods presented in the class lectures.  Specialized software will be used to examine real world data from multiple sources, with a strong emphasis on problem-based and interactive learning.  Students will also form groups to complete an independent literature review of a remote sensing field of their interest, presented orally to their classmates.v

Grading

  • Lab assignments: 25%
  • Midterm: 25%
  • Literature Review 15%
  • Final Exam 35%

Materials

RECOMMENDED READING:

Campbell, J.B. and Wynne, R.H. (2011). Introduction to Remote Sensing. 5th Edition. USA: Guilford Press.  ISBN: 9781609181765

Registrar Notes:

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS