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.

  1. Lillesand, Thomas M.; Kiefer, Ralph W.; and Chipman, Jonathon (2008) Remote Sensing and Image Interpretation, 6th New York: John Wiley and Sons.
  2. Avery, Thomas E. and Berlin, Graydon L. (1992) Fundamentals of Remote Sensing and Airphoto Interpretation, Upper Saddle River, New Jersey: Prentice Hall. (Also on reserve in map library)
  3. Paine, David P. (1981) Aerial Photography and Image Interpretation for Resource Management. New York: John Wiley and Sons.
  4. Wolf, Paul R. and DeWitt, Bon A. (2000) Elements of Photogrammetry: with applications in GIS, 3rd 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 20/30%* > 90% A+ 70-72% B-
Final 20/30%* 85-89% A 67-69% C+
Labs 50% 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 – THURSDAY, MARCH 4

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 17, 9 AM

Tentative Lab Schedule

Date Lab Topic
Jan 7 No lab during first week of classes.
Jan 14 1 Google Earth Tutorial – Image Interpretation and KDCs
Jan 21 2 Google Earth – Image Interpretation and Feature Extraction
Jan 28 3 Horizontal Measurements: Scale, Distances, Directions, Areas
Feb 4 4 Capturing Geospatial Data: Site Mapping
Feb 11 5 Vertical Measurements: Methods of Height Determination
NO LAB (Midterm Break)          
Feb 25 6 Introduction to RPAS
Mar 4 7 Digital Image Analysis
Mar 11 8 Image Classification: Training Field Selection
Mar 18 9 Image Classification: Training Field Evaluation
Mar 25 10 Image Classification: MED & Max Like
Apr 1 Project Work
Apr 8 RPAS Demo Flight (weather and COVID permitting)

Useful Links

National Air Photo Library (NAPL)

Canada Centre for Remote Sensing (CCRS)

Download MultiSpec

Imagery

CL-345-13Aug91 – Copy

(right click and “save as” then change file extension from .pdf  to .lan)

RMNP_MetaData – Copy

(right click and “save as” then change file extension from .pdf  to .txt)