GMT696 - SPECIAL TOPICS IN REMOTE SENSING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| SPECIAL TOPICS IN REMOTE SENSING | GMT696 | Any Semester/Year | 3 | 0 | 3 | 7 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| 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 | Introduction to Satellite Remote Sensing, Editor(s): William Emery, Adriano Camps, Elsevier,2017,ISBN 9780128092545,https://doi.org/10.1016/B978-0-12-809254-5.03001-3. | |||||
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 | Final preparation |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 16 | 5 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 5 | 10 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 35 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 23 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 16 | 3 | 48 |
| 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 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 5 | 8 | 40 |
| Midterms (Study duration) | 2 | 17 | 34 |
| Final Exam (Study duration) | 1 | 18 | 18 |
| Total Workload | 38 | 51 | 210 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Define problems in Geomatics Engineering and use Information Technology effectively in order to solve these problems. | X | ||||
| 2. Learn basic Mathematics, Science and Engineering formations and use them productively in professional life | X | ||||
| 3. Choose, use and improve recent technology and methods that needed for Geomatics Engineering applications | X | ||||
| 4. Earn the ability of producing new spatial products with data coming from international Geomatics application by using his/her qualification of obtaining, interpretation and analyzing of spatial data and by adding personal viewpoint | X | ||||
| 5. Estimate geodetic and geodynamic parameters with geodetic observations and use kinematic and dynamic functional models effectively in studies | X | ||||
| 6. Know advanced national and international applications in areas of Photogrammetry and Laser Scanning and contribute to the development processes of these applications | X | ||||
| 7. Develop strategies for data collection from space/aerial images and aerial/terrestrial laser scanning data; define the most appropriate methods for data extraction from collected data; process, analysis, integrate data with other spatial data, develop models; attend to field works and present results and outputs visually, statistically and thematically | X | ||||
| 8. Develop case / aim specific static or dynamic online systems, design spatial database management systems and produce visual products by following recent developments in GIS environment | X | ||||
| 9. Find solutions for aim relevant data obtainment by being familiar with working principle of scanning devices and sensors and their usage areas | X | ||||
| 10. Design systems which are considering scientific facts for more economically and more reliable management of industrial and infrastructure applications | X | ||||
| 11. Consider factors of social, environmental, economic, health and job security in professional life. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest