Remote Sensing II: Digital Image Processing
one-way Exclusions
One 2 hour lecture & one 2 hour lab per week
The textbook for this course is open access (no cost) and is available as a PDF and EPUB: Cardille, J. A., Crowley, M. A., Saah, D., & Clinton, N. E. (Eds.). (2023). Cloud-based remote sensing with google earth engine: fundamentals and applications. Springer Nature. .
Please note that course information listed in the Arts and Science Course Calendar supersedes any information listed on the Geography and Planning website.
For the most current course offerings, registered Queen鈥檚 students should consult .
Course Description
This course represents an extension of GPHY 242, with an in-depth examination of image processing techniques for information extraction. Topics include remote sensor technology, image enhancement, classification, change detection, radiometric and geometric correction and sources and applications of remote sensing data.
NOTE Enrollment limited to 40 students.
Course Overview
This course will introduce students to the analysis of satellite images using Google Earth Engine, a code-based environment that leverages Google鈥檚 enormous online catalogue of satellite data. The course will provide a gentle introduction to JavaScript, the language used in Google鈥檚 code editor, as well as a thorough review of concepts from second-year remote sensing (GPHY 242). The course will build towards a final project where students will analyze a time series in Google Earth Engine to reveal significant changes on the decadal scale. Emphasis will be placed on hands-on practice with Earth Engine during both lectures and lab sessions. Students should be prepared to bring a laptop to the lectures to access the Google code editor to follow along with demonstrations and exercises. The pre-requisite for this course, GPHY 242, may be waived for those students with significant coding experience.
Course Topics
- JavaScript Coding in Google Earth Engine
- Spectral Indices
- Supervised and Unsupervised Classification
- Pixel and Neighbourhood-based Image Analysis
- Object-based Image Analysis
- Filtering Image Collections for Time Series
- Cloud Cover and Image Compositing
- Change Detection
Learning Outcomes
- Compare how electromagnetic radiation reveals physical properties in passive and active remote sensing systems.
- Assess whether the source, scale, resolution, and analysis of remotely sensed data is adequate to describe environmental phenomena.
- Integrate remotely sensed data with other data sources to perform spatio-temporal analyses.
- Recognize the limitations of remotely sensed data to explain environmental phenomena.
Assessments
Subject to Change
- Labs (5): 10% each
- Multiple Choice and Competency-Based Assessment Tests (2): 15% each
- Attendance (Labs and Lectures): 5%
- Final Project (Groups of 1-3 Students): 15%