Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
HRT422 Uydu Görüntülerinin Analizi 927006 4 8 3
Level of Course Unit
First Cycle
Objectives of the Course
Instruction of analysis, classification and interpretation of RS images
Name of Lecturer(s)
Doç. Dr. Sedat DOĞAN
Learning Outcomes
  1. Student learns the fundamentals of the analysis methods in remote sensing.
  2. Can implement remote sensing algorithms with his/her own coded programs.
  3. Understands the basic concepts related to automatic map updateing and spatial data infrastructures.
  4. Are aware of semantic content of RS images.
  5. Learns how to use the RS images for emergency management tasks.
Mode of Delivery
Formal Education
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Recommended or Required Reading
Schowengerdt, R.A., 2007. Remote Sensing, Models and Methods forImage Processing. Elsevier Inc.Egan W.G., 2004. Optical Remote Sensing, Marcel Dekker Inc.Rees W.G., 2001. Physical Principles of Remote Sensing, CambridgeUniversity Press.Elachi, C and Van Zyl, J., 2006. Introduction to Physics and Techniques ofRemote Sensing, Wiley and Sons Inc. Publication.Remote Sensing softwares, Matlab and related data analysis softwares.
Planned Learning Activities and Teaching Methods
Language of Instruction
Turkish
Work Placement(s)
None
Course Contents
Data systems and data models in remote sensing.Corelations between bands.Introduction to supervised and unsupervised classification methods.Clustering algorithms.Supervised classification methods.Back propagation artificial neural networks.Back propagation algoritms.Supervised classification algorithms.Factor and PCA analyses.Implementation of conponent analysis methods with self training ANNs.Change detection with RS images.Automating updating maps with RS images.Semantic data in images.
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1.Data systems and data models in remote sensing.
2.Corelations between bands.
3.Introduction to supervised and unsupervised classification methods.
4.Clustering algorithms.
5.Supervised classification methods.
6.Back propagation artificial neural networks.
7.Back propagation algoritms.
8.Supervised classification algorithms.
9.Mid-term exam.
10.Factor and PCA analyses.
11.Implementation of conponent analysis methods with self training ANNs.
12.Change detection with RS images.
13.Automating updating maps with RS images.
14.Semantic data in images.
15.
16.
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 Examination11010
Final Examination11010
Self Study10110
Individual Study for Homework Problems3515
Individual Study for Mid term Examination155
Individual Study for Final Examination155
Homework21020
SUM75