Spring 2022 - GEOG 353 D100
Advanced Remote Sensing (4)
Class Number: 4757
Delivery Method: In Person
Course Times + Location:
Th 2:30 PM – 4:20 PM
AQ 3154, Burnaby
Exam Times + Location:
Apr 22, 2022
12:00 PM – 2:00 PM
WMC 2220, Burnaby
Office: RCB 6139
Office Hours: TBA
Advanced remote sensing principles and data processing techniques, including image correction and enhancement, advanced image analysis and information extraction, land cover classification and change detection, and integration of remote sensing and GIS. Quantitative.
GEOG 353 is the upper-level course of the remote sensing stream of courses offered by the Department of Geography (the introductory-level course is GEOG 253 - Introduction to Remote Sensing and the final course is GEOG 453 - Theoretical and Applied Remote Sensing). Compared to GEOG 253 that provides an overview of remote sensing and training on preliminary image interpretation, this course focuses on computer processing and analysis of images, and advanced applications of remote sensing for monitoring physical and human environments. Topics in this course include image preprocessing, image enhancement and transformation, image classification and change detection, advanced Radar and LiDAR, unmanned aerial vehicle (UAV)-based remote sensing, hyperspectral remote sensing, and integration of remote sensing and Geographic Information System (GIS). Remote sensing imagery is an essential data source and GIS is a powerful geo-analytical tool, with their integration being at the center of a larger trend toward the fusion of different geo-spatial data and technologies, and thus will be thoroughly discussed in this course. GEOG 353 will include lectures that cover foundational concepts and practical lab sessions where students will work on the acquisition and processing of remote sensing images. Upon completion of this course, students should have the technical expertise to process and analyze remote sensing data and pursue more advanced work in remote sensing applications.
Spring 2022 courses will be delivered in person based on information available at the time of publishing the outline; please note the delivery mode is subject to change following Provincial Health Officer (PHO) and/or SFU recommendations and orders.
COURSE-LEVEL EDUCATIONAL GOALS:
After successfully completing this course, students will:
- Have a more detailed overview of remote sensing and stronger understanding of physical principles underlying the remote sensing measurements
- Acquire technical expertise processing and analyzing remote sensing images
- Gain and develop a strong working knowledge of standard remote sensing software for real-world applications
- Have the ability to integrate remote sensing products in a GIS project framework for analyzing different environmental features
- Understand the uncertainties throughout the information extraction processes
- Understand advanced remote sensing technologies and their strengths and limitations
Note: There will be no labs during the first week of class
- Assignments 40%
- Midterm Test 25%
- Final Examination 35%
Computer Processing of Remotely-Sensed Images: An Introduction
Paul M. Mather; Magaly Koch
Print ISBN: 9780470742389, 0470742380; eText ISBN: 9781119956402, 1119956404
(Available on VitalSource)
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TEACHING AT SFU IN SPRING 2022
Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place. Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes. You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).
Enrolling in a course acknowledges that you are able to attend in whatever format is required. You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.
Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (email@example.com or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.