GMK694 - SPECIAL TOPICS IN REMOTE SENSING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| SPECIAL TOPICS IN REMOTE SENSING | GMK694 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | ||||||
| Course language | English | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer | |||||
| Instructor (s) | Prof. Dr. Mustafa TÃœRKER | |||||
| Course objective | Provide details information about the sensors, mathematical models and object extraction from images in Geomatics. | |||||
| Learning outcomes |
| |||||
| Course Content | Recent advances in; airborne and spaceborne sensors, radiometric calibration techniques, mathematical sensor models, image classification, feature extraction, orthoimage generation, segmentation. Artificial intelligence and areas of expertise of visiting scientists. | |||||
| References | - Lillesand, T.M. and Kiefer, R.W., 1987. Remote sensing and Image Interpretation, John Wiley. - Jensen, J. R. Introductory digital image processing a remote sensing perspective, Prentice Hall series in geographic information science. - Schowengerdt, R. A., 2007. Remote Sensing: Models and Methods for Image Processing, Academic Press. - Campbell, J.B., 1996. Introduction to Remote Sensing, Taylor & Francis, London. - Cracknell, P. and Hayes, L. Introduction to remote sensing | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Airborne and spaceborne sensors |
| Week 2 | Radiometric calibration techniques |
| Week 3 | Mathematical sensor models |
| Week 4 | Mathematical sensor models |
| Week 5 | Advanced image classification techniques |
| Week 6 | Midterm exam |
| Week 7 | Feature extraction from images |
| Week 8 | Feature extraction from images |
| Week 9 | Orthoimage generation |
| Week 10 | Segmentation techniques |
| Week 11 | Midterm exam |
| Week 12 | Segmentation techniques |
| Week 13 | Segmentation techniques |
| Week 14 | Artificial intelligence and areas of expertise of visiting scientists |
| Week 15 | Preparation for the final exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 14 | 5 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 6 | 15 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 30 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 0 | 50 |
| Percentage of final exam contributing grade succes | 0 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 6 | 84 |
| Presentation / Seminar Preparation | 2 | 1 | 2 |
| Project | 0 | 0 | 0 |
| Homework assignment | 6 | 10 | 60 |
| Midterms (Study duration) | 2 | 16 | 32 |
| Final Exam (Study duration) | 1 | 16 | 16 |
| Total Workload | 39 | 52 | 236 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. The ability to access the knowledge extensively and deeply and to evaluate and interpret knowledge in the scientific area of interest by means of carrying out a scientific research. | X | ||||
| 2. To have comprehensive knowledge on state-of-the-art techniques and methods used in the research area of interest with their possible constraints. | X | ||||
| 3. To be aware of the novel and emerging applications on his/her profession and to have the ability to search and learn these items when necessary. | X | ||||
| 4. The ability to design engineering problems, to develop and implement innovative methods for finding solutions. | X | ||||
| 5. The ability to develop new and/or novel ideas and methods; the ability to develop innovative solutions for the design problems of a system, a component or a process. | X | ||||
| 6. The ability to complete and apply the knowledge with scientific methods by using limited or incomplete data; to have the ability to integrate information from different disciplines. | X | ||||
| 7. The ability to describe the social and environmental consequences of engineering applications. | X | ||||
| 8. The ability to design and implement the researches based on analytical thinking, modeling and empirical reasoning; the ability to resolve and interpret the complex conditions encountered in this process. | X | ||||
| 9. To act responsibly in the stages of data collection, interpretation, and dissemination as well as consider scientific and ethical values in all professional activities. | X | ||||
| 10. To share the methodology and the results of his/her studies systematically and explicitly through national and international scientific platforms by means of written or oral discourse. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest