Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
HRT407 Remote Sensing 927006 4 7 4
Level of Course Unit
First Cycle
Objectives of the Course
The main objectives of this course are to provide knowledge on concepts and principles of Remote Sensing and image processing and to familiarize the students with a Remote Sensing software by hands-on experiences.
Name of Lecturer(s)
Doç. Dr. Derya Öztürk
Learning Outcomes
  1. Describe and explain remote sensing origins, concepts and terminology.
  2. Know the various types of satellite systems currently uztilized for remote sensing applications.
  3. Understand the concepts of satellite image correction.
  4. understand and apply the digital image processing techniques for satellite images.
Mode of Delivery
Formal Education
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Recommended or Required Reading
Campbell, J. B., 1996. Introduction to Remote Sensing, Second edition, The Guilford Press.Lillesand, T.M , Kiefer, R.W., 1997. Remote Sensing and Image Interpretation, John Wiley Sons, USA.
Planned Learning Activities and Teaching Methods
Language of Instruction
Turkish
Work Placement(s)
None
Course Contents
Principles of remote sensing; digital image processing.
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1.- Remote Sensing: history, development, definition, concept and principles- Remote sensing applications
2.- Energy resources, radiation principles, EM radiation and EM spectrum- Interaction of EMR with atmosphere and Earth’s surface
3.Satellites and their characteristics
4.Sensors – Types and their characteristics, Across track (whiskbroom) and Along track (pushbroom) scanning
5.Concept of Resolution – Spatial, Spectral, Temporal , Radiometric
6.- Basic concept and principles of Thermal , microwave and hyperspectral sensing- LiDAR
7.- Basic principles, types, steps and elements of image interpretation- Techniques of visual interpretation and interpretation keys
8.- Digital image processing- Image processing systems –hardware and software considerations
9.- Radiometric correction of remotely sensed data- Geometric correction of remotely sensed data
10.MIDTERM EXAM
11.- Image enhancement
12.- Principal Component Analysis- Vegetation indices
13.Pattern recognition and image classification- Unsupervised classification- Supervised classification-training site selection
14.Classification accuracy assessment
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight (%)
Midterm Examination1100
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight (%)
Final Examination1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Workload Calculation
ActivitiesQuantityTime(hours)Total Workload(hours)
Midterm Examination122
Final Examination122
Attending Lectures14342
Self Study14114
Individual Study for Mid term Examination11515
Individual Study for Final Examination12020
Homework155
SUM100