| Course Type | Elective Course |
|---|
| Course Level | Doctorate Of Science |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Know the pre-processing and enhancement techniques of satellite imagery.
- Know the spectral classification in space borne imagery.
- Know the selection and evaluation of training sites used in classification.
- Know the supervised and unsupervised classification techniques.
- Know the digital change detection.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Image preprocessing and enhancement of satellite imagery, Spectral classification in spaceborne imagery, Selection and evaluation of training sites, Different methods of unsupervised (sequential cluster, statistical cluster, isodata) and supervised (parallelepiped, minimum distance, Mahalanobis distance, maximum likelihood/ Bayesian) classification, Digital change detection. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Image preprocessing and enhancement of satellite imagery. |
|
2nd Week |
Image preprocessing and enhancement of satellite imagery. |
|
3rd Week |
Spectral classification in spaceborne imagery. |
|
4th Week |
Spectral classification in spaceborne imagery. |
|
5th Week |
Selection and evaluation of training sites. |
|
6th Week |
Selection and evaluation of training sites. |
|
7th Week |
Selection and evaluation of training sites. |
|
8th Week |
Mid-term |
|
9th Week |
Different methods of unsupervised (sequential cluster, statistical cluster, isodata) and supervised (parallelepiped,minimum distance |
|
10th Week |
Different methods of unsupervised (sequential cluster, statistical cluster, isodata) and supervised (parallelepiped,minimum distance |
|
11th Week |
Mahalanobis distance, maximum likelihood/ Bayesian) classification. |
|
12th Week |
Mahalanobis distance, maximum likelihood/ Bayesian) classification. |
|
13th Week |
Digital change detection. |
|
14th Week |
Digital change detection. |
|
| Recommended or Required Reading | 1. Jensen, J., "Introductory Digital Image Processing", 3rd edition, Prentice Hall, (2004).
2. Richards, J.A., & Jia, Xiuping, J., "Remote Sensing Digital Image Analysis: An Introduction", 4th edition, Springer (2005).
3. Lillesand, T., Kiefer, R.W., & Chipman, J., "Remote Sensing and Image Interpretation", 6th edition, Wiley, (2007).
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Problem Solving
- Practice
- Project
- Report Preparation
- Presentation
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
1 |
15 |
| Project |
1 |
15 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
11 |
154 |
| Preparation for Final exam |
1 |
69 |
69 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
44 |
44 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Total Work Load | 313 |
| Total Work Load / 30 (h) | 10.43 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
