Special Topics In Geodesy and Geoinformatics II
Geodesy and Geoinformation Engineering
Institute of Natural and Applied Sciences
Third Cycle (Doctorate Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Special Topics In Geodesy and Geoinformatics II | JJM614 | Elective | Doctorate degree | 1 | Spring | 8 |
Name of Lecturer(s)
Prof. Dr. Ozan ARSLAN
Associate Prof. Dr. Orhan KURT
Learning Outcomes of the Course Unit
1) Describe the basic information on remote sensing platforms and sensors
2) Evaluate the basic understanding on image processing, interpretation and analysis methods
3) Use the radiometric enhancement techniques of remotely sensed
images and geometric correction algorithms
4) Explain the basic information on sensor systems, digital cameras and scanner systems
5) Describe the basic information on electromagnetic radiation and physical principles
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||
| 1 | 2 | 3 | 4 | ||
| Learning Outcomes | |||||
| 1 | No relation | No relation | High | No relation | |
| 2 | No relation | No relation | Middle | No relation | |
| 3 | No relation | No relation | High | No relation | |
| 4 | No relation | No relation | High | No relation | |
| 5 | No relation | No relation | High | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
No course of any proposed
Course Contents
Candidates are provided with profound knowledge on advanced radiometric enhancement techniques in remote sensing imagery, image calibration with atmospheric correction models, Fourier transformations of image data, geometric enhancement using image domain techniques, detecting geometric properties, the wavelet transform for the analysis of remotely sensed images, Non-parametric classification algorithms, separability measures for multivariate spectral class models, and Canonical analysis for feature selection procedure.
Weekly Schedule
1) Advanced radiometric enhancement techniques in remote sensing imagery2) Image calibration with atmospheric correction models
3) Geometric enhancement using image domain techniques
4) Fourier transformations of image data
5) Fast Fourier transformations of image data
6) Detecting geometric properties of image data
7) Detecting geometric properties og remotely sensed image
8) Midterm examination/Assessment
9) The wavelet transform for the analysis of remotely sensed images
10) Parametric and Non-parametric classification algorithms in remote sensing
11) Non-parametric classification methods
12) Canonical analysis for feature selection procedure
13) Separability measures for multivariate spectral class models
14) Statisticsl separability measures for multivariate spectral class models
15) Sample software applications for separability measures
16) Final examination
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Discussion
3) Demonstration
4) Group Study
5) Problem Solving
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
50% |
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Contribution of Final Examination to Course Grade |
50% |
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Total | 100% | ||||||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required