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| JDZL7390 | Hyperspectral Remote Sensing | 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 | Doç. Dr. Esra TUNÇ GÖRMÜŞ | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The objective of this course is to teach hyperspectral image processing techniques. Feature selection to find optimum band combinations to get information that is not possible to obtain from multispectral images.
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| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 4 : | Processing hyperspectral images using various remote sensing software for information extraction purposes, perform feature selection to chose optimum band combination to get best useful bands | 4,6 | 1,3, | | PO - 6 : | Getting information about the use of hyperspectral images in different disciplines, to understand how they can use hyperspectral images to solve some particular problems and comprehend the superiority of hyperspectral images over multispectral ones. | | 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 | | |
| History of hyperspectral remote sensing. Spectral radiometry. Hyperspectral remote sensing sensors. Hyperspectral remote sensing and atmosphere. Feature extraction from hyperspectral images. Feature selection. Hyperspectral and ultraspectral feature extraction approachs. Hyperspectral image applications in agriculture. Hyperspectral image applications in environment and city planning. Hyperspectral image applications in forestry. Hyperspectral image applications in geology.
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | History and Description of Hyperspectral Imaging | | | Week 2 | Spectral radiometry | | | Week 3 | Hyperspectral imaging systems | | | Week 4 | Hiperspektral uzaktan algılama ve atmosfer | | | Week 5 | Information Extraction from Optical Image Data | | | Week 6 | Hyperspectral and Ultraspectral Information Extraction Approaches | | | Week 7 | Hyperspectral and Ultraspectral Information Extraction Approaches | | | Week 8 | Mid-term exam | | | Week 9 | Optimum band selection algorithms | | | Week 10 | Feature reduction for classification purposes | | | Week 11 | Agricultural Applications | | | Week 12 | Geology Applications | | | Week 13 | Environmental Applications | | | Week 14 | Forestry Applications | | | Week 15 | Geberal Review | | | Week 16 | End-of-term exam | | | |
| 1 | Borengasser, M., Huntage, W. S., Watkins, R. 2008; Hyperspectral remote sensing: Principles and applications. CRC Press. | | | 2 | Chang, C. I. 2007; Hyperspectral Data Exploitation. John Wiley and Sons, USA. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | | 1 | 20 | | Project | 6 | | 20 | 20 | | Homework/Assignment/Term-paper | 5
7
9
10
11
12
13
14 | | 40 | 10 | | End-of-term exam | 16 | | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 5 | 14 | 70 | | Arasınav için hazırlık | 6 | 1 | 6 | | Arasınav | 1 | 1 | 1 | | Ödev | 5 | 8 | 40 | | Proje | 4 | 5 | 20 | | Dönem sonu sınavı için hazırlık | 9 | 1 | 9 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 189 |
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