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| JDZL5982 | Pansharpening Applications for Remote Sensing | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of GEOMATICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Çiğdem ŞERİFOĞLU YILMAZ | | Co-Lecturer | Doç. Dr. Volkan Yılmaz | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The main purpose is to obtain basic information about pansharpening and to produce high-resolution multispectral images when such images are not available. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Understand the basics of pansharpening | 2,6 | 1, | | PO - 2 : | Gain information about pansharpening methods | 1,3,6 | 1, | | PO - 3 : | Use the pansharpening software at an advanced level | 5 | 1,3, | | PO - 4 : | Encode pansharpening methods in a programming language | 5,6 | 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 | | |
| Basic principles of pansharpening, Points to be considered in pansharpening, Component substitution-based pansharpening methods, Multi-resolution analysis-based pansharpening methods, Hybrid pansharpening methods, Model-based pansharpening methods, Qualitative and quantitative evaluation of the quality of pansharpened images. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction, scope of the course, concepts, general definitions, references | | | Week 2 | Application areas of pansharpening | | | Week 3 | Factors affecting the success of pansharpening | | | Week 4 | Component substitution-based pansharpening methods | | | Week 5 | Component substitution-based pansharpening methods | | | Week 6 | Multiresolution analysis-based pansharpening methods | | | Week 7 | Multiresolution analysis-based pansharpening methods | | | Week 8 | Model-based pansharpening methods | | | Week 9 | Mid-term exam | | | Week 10 | Model-based pansharpening methods | | | Week 11 | Hybrid pansharpening methods | | | Week 12 | Hybrid pansharpening methods | | | Week 13 | Applications with pansharpening software | | | Week 14 | Applications with pansharpening software | | | Week 15 | Qualitative and quantitative evaluation of the quality of pansharpened images | | | Week 16 | Final exam | | | |
| 1 | Pohl, C., & Van Genderen, J. 2016; Remote sensing image fusion: A practical guide, Crc Press. | | | 2 | Alparone, L., Aiazzi, B., Baronti, S., & Garzelli, A. 2015; Remote sensing image fusion, Crc Press. | | | 3 | Azarang, A., & Kehtarnavaz, N. 2021; Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches. Synthesis Lectures on Image, Video, and Multimedia Processing, 10 (1), 1-93. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | | 1 | 30 | | Homework/Assignment/Term-paper | 12 | | | 20 | | 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 | 6 | 14 | 84 | | Arasınav için hazırlık | 6 | 6 | 36 | | Arasınav | 1 | 1 | 1 | | Proje | 4 | 8 | 32 | | Dönem sonu sınavı için hazırlık | 6 | 6 | 36 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 233 |
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