38/42:353 Remote Sensing

Instructor: Lecture, Dr. Dion J. Wiseman
Office: Brodie Room 4-07
Office Hours: M, W, F; 11:00 am – 12:00 pm via Zoom; or by Appointment
Phone/Text: (204) 727-9774
Email: wiseman@brandonu.ca

Course Resources

Optional Texts: all on reserve in the main library.

Lillesand, Thomas M.; Kiefer, Ralph W.; and Chipman, Jonathon (2008) Remote Sensing and Image Interpretation, 6^th New York: John Wiley and Sons.
Avery, Thomas E. and Berlin, Graydon L. (1992) Fundamentals of Remote Sensing and Airphoto Interpretation, Upper Saddle River, New Jersey: Prentice Hall.
Paine, David P. (1981) Aerial Photography and Image Interpretation for Resource Management. New York: John Wiley and Sons.
Wolf, Paul R. and DeWitt, Bon A. (2000) Elements of Photogrammetry: with applications in GIS, 3^rd Boston: McGraw-Hill.

Course Description

The intent of this course is to: 1) introduce the theoretical foundations of remote sensing and 2) provide the technical expertise required to extract qualitative and quantitative information from remotely sensed imagery. The course is divided into three complimentary sections. The first is an introduction to the founding principles and basic elements of a complete remote sensing system, the second focuses on photographic systems, airphoto interpretation, and photogrammetric techniques; and the third introduces students to UAV systems, digital multispectral imagery, and computer assisted image analysis techniques.

The laboratory component of the course will be divided into two sections. The first will focus on aerial photography, image interpretation, and hard/soft-copy photogrammetric techniques. The second section will introduce computer assisted image analysis techniques commonly used for the extraction of thematic map information and spectral/object-based image classification. Relevant techniques will be demonstrated during class time and each student will complete a practical assignment normally due at the beginning of the following lab period. This section of the course will culminate in the submission of a multipart lab project.

Grading Scheme

Midterm	25/35%^*	> 90%	A+	70-72%	B-
Final	25/35%^*	85-89%	A	67-69%	C+
Labs	40%	80-84%	A-	63-66%	C
Bonus	3%	77-79%	B+	60-62%	C-
Total	100%	73-76%	B	50-59%	D
		^*variably weighted		< 50%	F

Tentative Course Outline

Fundamentals of Remote Sensing
- History of Remote Sensing
- Advantages of Remote Sensing
- Elements of a RS System
- The Nature of EMR
- EMR-Matter Interactions
Elements of Photo Interpretation
- Interpretation Process
- Key Diagnostic Characteristics
Principles Photographic & Digital Imaging Systems
- Principles of Photography
- Additive and Subtractive Colour Theory
- Films, Filters, Aerial Cameras
- Photo and Image Resolution
Scale and Horizontal Measurements
- Types of Scale
- Scale Determination
- Measuring Distances, Directions, and Areas
Geometry of Aerial Photographs
- Types of Areal Photographs
- Three Photo Centers
- Distortion and Displacement
Principles of Stereoscopic Vision
- Parallax
- Optical Equipment
- Vertical Exaggeration
Photogrammetry: Vertical Measurements
- Topographic Displacement
- Shadow Method
- Difference of Parallax
MIDTERM EXAM – Thursday, February 29th 1:40 pm (during lab period)
Introduction to RPAS
- What is a UAV? Drone? RPAS?
- Types, Components, Sensors
- Advantages & Disadvantages
- Transport Canada Regulations
- Photo Mission Planning
- Image Classification, Terrain Modeling, Feature Extraction
Fundamentals of Digital Multispectral Systems
- Platforms and Sensors
  - UAV Platforms & Sensors
  - LandSat Overview
  - Free LandSat Imagery
- Spectral Bands
- Image Interpretation
- Spectral Signatures
Digital Image Analysis
- Image Pre-processing,
- Image Classification
- Accuracy Assessment
- Object-based Classifications
RADAR
- Advantages
- Active vs. Passive Systems
- Causes of Backscatter
- Azimuth and Range Resolution

FINAL EXAM – APRIL 11^TH, 9 AM

Tentative Lab Schedule

Labs are scheduled THURSDAY afternoons, 1:40 to 4:30 PM, in room BB 3-47/3-44.

Date	Lab	Topic
Jan 4	—	—
Jan 11	1	Introduction to Google Earth (GE)
Jan 18	2	Image Interpretation and Analysis
Jan 25	3	Importing and Extracting Geospatial Data in GE
Feb 1	4	Air Photo Interpretation and Stereoscopy
Feb 8	5	Scale and Horizontal Measurements
Feb 15	6	Methods of Determining the Height of Features
Feb 22	—	NO LAB (Winter study break)
Feb 29	7	Intro to Remotely Piloted Aircraft Systems (RPAS)
Mar 7	8	Image Classification: Unsupervised Classification
Mar 14	9	Image Classification: Training Field Selection
Mar 21	10	Image Classification: Training Field Evaluation
Mar 28	11	Image Classification: Supervised Classification
Apr 4	12	RPAS Demo Flight (weather permitting)

Generally, lab assignments are due at the end of the current lab period or beginning of the following week’s lab period. Labs submitted at the end of the following week’s lab period will be considered one day late. Late labs will incur a penalty of 10% per day.