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| JDZ7262 | Feature Extraction of Remotely Sensed Data | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of GEOMATICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Fevzi KARSLI | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Objectives of this course are to explain the basic fundamentals of remotely sensed data, to apply image processing methods on images for extracting the objects, and to learn feature extraction applications on some areas such as urban, rural and forest. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Have ability to understanding of geometric and spectral properties of remotely sensed images. | 1 | 1, 3 | | PO - 2 : | Be aware of geo-referencing of images, image enhancement and segmentation. | 2 | 1, 3 | | PO - 3 : | Understand feature extraction process and its application on images. | 3 | 1, 3 | | PO - 4 : | Extract some features such building and roads, and transfer it to GIS media. | 1 | 1, 3 | | PO - 5 : | Manage feature extraction project from acquisition to presentation. | 2 | 1, 3 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Remotely sensed images, geometric and radiometric properties of images, B/W and multi-spectral images, geo-referencing of images, image enhancement and segmentation, feature extraction process, approaches of building and road extraction, edge detection and its operators, vectorisation, evaluation and validation of data, transferring data to GIS media, feature extraction applications on some areas such as urban, rural and forest. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Remotely sensed images | | | Week 2 | Geometric and radiometric properties of images | | | Week 3 | B/W and multi-spectral images | | | Week 4 | Geo-referencing of images | | | Week 5 | Geo-referencing of images | | | Week 6 | Image enhancement and segmentation | | | Week 7 | Deature extraction process | | | Week 8 | Approaches of building and road extraction | | | Week 9 | Mid-term exam | | | Week 10 | Edge detection and its operators | | | Week 11 | Vectorisation | | | Week 12 | Evaluation and validation of data | | | Week 13 | Evaluation and validation of data | | | Week 14 | Transferring data to GIS media | | | Week 15 | Feature extraction applications on some areas such as urban, rural and forest | | | Week 16 | End-of-term exam | | | |
| 1 | MATHER, P. M., 1999. Computer Processing of Remotely- Sensed Images. Second edition. John Wiley-Sons Ltd. England. | | | 2 | http://www.lsv.uni-saarland.de/dsp_ss05_chap8.pdf | | | |
| 1 | www.mathworks.com (MATLAB) | | | 2 | http://www.icaen.uiowa.edu/dip/LECTURE/ImageProperties.html | | | 3 | Gonzalez, R. C., Woods, R. E., Eddins, S. L., Digital Image Processing Using Matlab, Prentice Hall, 2004. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | Homework/Assignment/Term-paper | 15 | 11/05/2016 | 10 | 20 | | End-of-term exam | 16 | 24/05/2016 | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Ödev | 3 | 5 | 15 | | Total work load | | | 15 |
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