Course Outline

GEOG 140 - Introduction to Remote Sensing and Drone Data Processing


Fall Semester 2018

Basic Course Information
Courses numbered 1 - 49 are remedial or college preparatory courses which do not apply toward an A. A. Degree and are not intended for transfer. Courses numbered 50-99 apply toward an AA Degree, but are not intended for transfer. Courses numbered 100 and higher apply toward an AA Degree and/or are intended for transfer to a four-year college or university.

D - Credit - Degree Applicable
GEOG
Introduction to Remote Sensing and Drone Data Processing
Units and Hours
3
3
Grade/Pass/No Pass
Hour Type
Units
Weekly Hours
Semester Hours x 16 Weeks
Semester Hours x 18 Weeks
Lecture Category -
3.00
3.00
x 16 Weeks - 48.00
x 18 Weeks - 54.00
Lab Category -
0.00
0.00
x 16 Weeks - 0.00
x 18 Weeks - 0.00
Subtotal -
 
3.00
x 16 Weeks - 48.00
x 18 Weeks - 54.00
Out of Class Hour -
 
6.00
x 16 Weeks - 96.00
x 18 Weeks - 108.00
Totals -
 
9.00
x 16 Weeks - 144.00
x 18 Weeks - 162.00
Hour Type
Units
Weekly Hours
Semester Hours x 16 Weeks
Semester Hours x 18 Weeks
Lecture Category -
3.00
3.00
x 16 Weeks - 48.00
x 18 Weeks - 54.00
Lab Category -
0.00
0.00
x 16 Weeks - 0.00
x 18 Weeks - 0.00
Subtotal -
 
3.00
x 16 Weeks - 48.00
x 18 Weeks - 54.00
Out of Class Hour -
 
6.00
x 16 Weeks - 96.00
x 18 Weeks - 108.00
Totals -
 
9.00
x 16 Weeks - 144.00
x 18 Weeks - 162.00
Catalog Description
Provides students with a basic understanding of theories and techniques used in the processing and analysis of satellite and drone (i.e. Unmanned aircraft systems) data. Topics include image and sensor characteristics, information derived from satellite and drone data , and image interpretation and analysis.
Student Learning Outcomes
Outcome
Students should be able to explain the relationship between wavelength, frequency, and energy content of electromagnetic waves.
Students should be able to list three common applications of data collected by satellite sensors.
Students should be able to list three common applications of data collected by UAS sensors.
Specific Course Objectives
Objective
Upon successful completion of the course, the student will be able to:
  • Distinguish between different types of remote sensing systems and unmanned aircraft systems (UAS);
  • Identify the appropriate satellite and UAS sensors for the application under consideration;
  • Specify the strengths and limitations of various remote sensing systems and UAS;
  • Explain the basics of the electromagnetic spectrum;
  • Preprocess and analyze remote sensing and UAS data;
  • Implement and interpret the results from unsupervised classification, supervised classification, and other object based classification techniques;
  • Assess and document the spatial and attribute accuracy of remote sensing and UAS data;
  • Construct and analyze 3D terrain and building models created with Light Detection and Ranging (LiDAR) and UAS data.
Methods of Instruction
Methods of Instruction may include, but are not limited to, the following:
Demonstration
Discussion
Group Projects/Activities
Guest Speakers
Learning Modules
Lecture
Other (Specify)
Content in Terms of Specific Body of Knowledge
  1. Electromagnetic Radiation Principles
    1. Insolation and irradiance
    2. Electromagnetic spectrum
      1. Ultraviolet
      2. Visible
      3. Near infrared and thermal infrared
      4. Microwave and radio waves
    3. Radiation
      1. Emission
      2. Reflection
      3. Transmission
      4. Absorption
  2. Satellite Sensors and Unmanned Aircraft Systems (UAS) Sensors
    1. Active and passive systems
      1. Multispectral sensors
      2. Hyperspectral sensors
      3. Infrared sensors
      4. Visible light sensors
      5. Laser scanners (LiDAR)
  3. Processing of remote sensing and UAS data
    1. Geometric correction
    2. Radiometric correction
    3. Examples of output
      1. Orthorectified images 
      2. Stereoscopic images
      3. 3D models
  4. Image Enhancement
    1. High pass filter
    2. Low pass filter
    3. Edge enhancement filter
    4. Directional filter
    5. Histogram equalization stretch
  5. Image Classification
    1. Supervised classification
      1. Strengths
      2. Limitations
    2. Object based classification
      1. Strengths
      2. Limitations
    3. Unsupervised classification
      1. Strengths
      2. Limitations
  6. Change Detection
    1. Image differencing
    2. Multi-date visual change detection
  7. Accuracy Assessment
    1. Spatial accuracy 
    2. Attribute accuracy
Textbooks/Resources
Textbook
Terwilliger, Brent
Small Unmanned Aircraft Systems Guide: Exploring Designs, Operations, Regulations, and Economics
Newcastle
Aviation Supplies and Academics
2017
Jensen, John
Introductory Digital Image Processing: A Remote Sensing Perspective
4th
Upper Saddle River
Prentice Hall
2015
Keranen, Kathryn; Kolvoord, Robert
Making Spatial Decisions Using GIS and Remote Sensing: A Workbook
Esri Press
2013
Text Other
Course reader/Handouts
Assignments
Students are required to research online articles and read trade publications in order to learn about new applications of UAS and remote sensing.
Students will complete approximately 12 learning modules (2-3 pages each) throughout the course. They will be required to produce concise and well-written responses.
Student are expected to apply deductive reasoning to complete examinations, learning modules, and homework problems. They will also be trained to troubleshoot technical problems and seek out relevant resources in an independent manner.
Students are required to complete homework problems and weekly learning modules, as well as conduct online research on a variety of class topics (e.g. hardware, software, data processing algorithms). Students are also expected to present case studies on emerging applications of UAS and remote sensing.
Methods of Assessment
Evaluation Method
  • Class Participation
  • Class Work
  • Demonstration
  • Exams/Tests
  • Homework
  • Oral Presentation
Open Entry/Open Exit
- Not Open Entry/Open Exit
Repeatability
No
Contact Person
Cheung, Wing H.