Image Processing and Spatial Information For Remote Sensing
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 |
|---|---|---|---|---|---|---|
| Image Processing and Spatial Information For Remote Sensing | JJM612 | Elective | Doctorate degree | 1 | Spring | 8 |
Name of Lecturer(s)
Prof. Dr. Ozan ARSLAN
Learning Outcomes of the Course Unit
1) Improve the ability of using advanced digital image processing algorithms in remote sensing
2) Discuss some spatial analysis concepts in remotely sensed images and spatial data modelling information
3) Explain the spatial correlation analysis and spatial statistics for digital images in remote sensing
4) Use the basic information on acquiring spatial data and analysis concepts in remote sensing
5) Identify some software development procedures on spatial data sets in remote sensing
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 | Middle | No relation | |
| 4 | No relation | No relation | Middle | No relation | |
| 5 | No relation | No relation | Low | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Remote Sensing
Course Contents
Candidates are provided with profound knowledge on introduction, acquiring spatial data, spatial data formats and models, information system approximation, modelling algorithms for spatial data, software engineering for spatial data sets, visualisation of geographic data, geostatistic for processing of remotely sensed images, spectral and spatial transforms, spatial autocorrelation methods, spatial statistics for image processing, pattern recognition for remote sensing, band rationing, principal component analysis, image enhancement and restoration, contrast enhancement methods, global and local transforms, filtering, image fusion and quantitative analysis.
Weekly Schedule
1) Acquiring spatial data and analysis2) Spatial data models and formats
3) Information systems and spatial data modelling algorithms
4) Software engineering on spatial data sets
5) Visualisation of geographic data
6) Geostatistic methods on processing of remote sensing data
7) Spectral and spatial transform methods
8) Semester exam
9) Spatial correlation analysis in remote sensing, spatial statistic on image processing
10) Pattern recognition, spectral transforms, band rationing
11) Image enhancement and restoration
12) Contrast enhancement methods
13) Spatial transforms and filtering
14) Image fusion and quantitative methods
15) Görüntü keskinleştirme yöntemleri
16) Final exam
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Drill and Practice
4) Demonstration
5) Lab / Workshop
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