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

Course Website: https://people.brandonu.ca/wiseman/courses/remote-sensing/

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

1) Fundamentals of Remote Sensing

a) History of Remote Sensing

b) Advantages of Remote Sensing

c) Elements of a RS System

d) The Nature of EMR

e) EMR-Matter Interactions

2) Elements of Photo Interpretation

a) Interpretation Process

b) Key Diagnostic Characteristics

3) Principles Photographic & Digital Imaging Systems

a) Principles of Photography

b) Additive and Subtractive Colour Theory

c) Films, Filters, Aerial Cameras

d) Photo and Image Resolution

4) Scale and Horizontal Measurements

a) Types of Scale

b) Scale Determination

c) Measuring Distances, Directions, and Areas

5) Geometry of Aerial Photographs

a) Types of Areal Photographs

b) Three Photo Centers

c) Distortion and Displacement

6) Principles of Stereoscopic Vision

a) Parallax

b) Optical Equipment

c) Vertical Exaggeration

7) Photogrammetry: Vertical Measurements

a) Topographic Displacement

b) Shadow Method

c) Difference of Parallax

MIDTERM – TBA

8) Introduction to RPAS

a) What is a UAV? Drone? RPAS?

b) Types, Components, Sensors

c) Advantages & Disadvantages

d) Transport Canada Regulations

e) Photo Mission Planning

f) Image Classification, Terrain Modeling, Feature Extraction

9) Fundamentals of Digital Multispectral Systems

a) Platforms and Sensors

i) UAV Platforms & Sensors

ii) LandSat Overview

iii) Free LandSat Imagery

b) Spectral Bands

c) Image Interpretation

d) Spectral Signatures

10) Digital Image Analysis

a) Image Pre-processing,

b) Image Classification

c) Accuracy Assessment

d) Object-based Classifications

11) RADAR

a) Advantages

b) Active vs. Passive Systems

c) Causes of Backscatter

d) Azimuth and Range Resolution

FINAL EXAM – APRIL 23, 9 AM

Tentative Lab Schedule

Date	Lab	Topic
Jan 13	1	Introduction to Google Earth (GE)
Jan 20	2	Image Interpretation and Feature Extraction
Jan 27	3	Scale and Horizontal Measurements
Feb 3	4	Capturing Geospatial Data: Site Mapping in GE
Feb 10	5	Methods of Determining the Height of Features
Feb 17	6	Introduction to RPAS/UAVs/Drones
Feb 24		NO LAB (Midterm Break)
Mar 3	7	Introduction to Digital Image Analysis
Mar 10	8	Image Classification: Training Field Selection
Mar 17	9	Image Classification: Training Field Evaluation
Mar 24	10	Image Classification: Supervised Classification
Mar 31	11	Image Classification: Unsupervised Classification
Apr 7		RPAS Demo Flight (weather and COVID permitting)