COURSE OUTLINE FOR CREDIT COURSE

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.

Discipline: GEOG
Course Number: 140
Title: Introduction to Remote Sensing and Drone Data Processing

Units and Hours

Units: 3.00
Grade Option: Grade/Pass/No Pass
Course Length in Weeks: Min Weeks - 16 Max Weeks - 18
Min Semester Hours
Hour Type
Hours
Min Semester Hours
Max Semester Hours
Lecture Category
3.00
48.00
54.00
Lab Category
0.00
0.00
0.00
Subtotal
3.00
48.00
54.00
Out of Class Hour
6.00
96.00
108.00
Totals
9.00
144.00
162.00
Max Semester Hours
Hour Type
Hours
Min Semester Hours
Max Semester Hours
Max Lecture Category
3.00
48.00
54.00
Max Lab Category
0.00
0.00
0.00
Max Subtotal
3.00
48.00
54.00
Max Out of Class Hour
6.00
96.00
108.00
Max Totals
9.00
144.00
162.00

Grading Basis: Grade/Pass/No Pass
Basic Skills Requirements: Appropriate Language and/or Computational Skills.

Requisites

To satisfy a prerequisite, the student must have earned a letter grade of A, B, C or P(Pass) in the prerequisite course, unless otherwise stated.

Prerequisite: None
Corequisite (Course required to be taken concurrently): None
Prerequisite: (Completion of, or concurrent enrollment in): None
Recommended Preparation: None
Limitation on Enrollment (e.g. Performance tryout or audition): None

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

Upon successful completion of the course, the student will be able to:
  1. Students should be able to explain the relationship between wavelength, frequency, and energy content of electromagnetic waves.
  2. Students should be able to list three common applications of data collected by satellite sensors.
  3. Students should be able to list three common applications of data collected by UAS sensors.

Specific Course Objectives

Upon successful completion of the course, the student will be able to:
  1. Distinguish between different types of remote sensing systems and unmanned aircraft systems (UAS);
  2. Identify the appropriate satellite and UAS sensors for the application under consideration;
  3. Specify the strengths and limitations of various remote sensing systems and UAS;
  4. Explain the basics of the electromagnetic spectrum;
  5. Preprocess and analyze remote sensing and UAS data;
  6. Implement and interpret the results from unsupervised classification, supervised classification, and other object based classification techniques;
  7. Assess and document the spatial and attribute accuracy of remote sensing and UAS data;
  8. 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
  1. Group Projects/Activities
  2. Demonstration
  3. Guest Speakers
  4. Learning Modules
  5. Lecture
  6. Discussion
  7. Other (Specify)
Other Method(s)
Student presentations of real-world applications of remote sensing and drones in environmental research will be required.

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

Textbooks
  1. Jensen, John. Introductory Digital Image Processing: A Remote Sensing Perspective. 4th Prentice Hall, 2015.
  2. Keranen, Kathryn; Kolvoord, Robert. Making Spatial Decisions Using GIS and Remote Sensing: A Workbook. Esri Press, 2013.
  3. Terwilliger, Brent. Small Unmanned Aircraft Systems Guide: Exploring Designs, Operations, Regulations, and Economics. Aviation Supplies and Academics, 2017.
Other
  1. Course reader/Handouts

Assignments

Required Reading:
Students are required to research online articles and read trade publications in order to learn about new applications of UAS and remote sensing.

Required Writing:
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.

Critical Thinking:
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.

Outside Assignments:
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.

Students are expected to spend a minimum of three hours per unit per week in class and on outside assignments, prorated for short-term classes.

Methods of Assessment

Methods of Assessment may include, but are not limited to, the following:
  1. Class Participation
  2. Class Work
  3. Demonstration
  4. Exams/Tests
  5. Homework
  6. Oral Presentation

Open Entry/Open Exit

Not Open Entry/Open Exit

Repeatability

Course is Repeatable for Reasons other than a Deficient Grade? No

Contact Person

Wing H. Cheung