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| HRT4043 | Remote Sensing with Hyperspectral Images | 2+0+0 | ECTS:4 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of GEOMATICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 2 hours of lectures per week | | Lecturer | Doç. Dr. Esra TUNÇ GÖRMÜŞ | | Co-Lecturer | | | 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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| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Learn to use hyperspectral images in different disciplines.understand how they can use hyperspectral images to solve some particular problems and comrehend the superiority of hyperspectral images over multispectral ones
| 1,4 | 1,3, | | LO - 4 : | To process hyperspectral images in various remote sensing softwares | 1,4 | 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), LO : 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 approaches. Learning Multispec program by following its tutorials. Learning about the use of Hyperspectral images in different fields like agriculture, geology and forestry.
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | History and basics of Hyperspectral imaging | | | Week 2 | Spectrometry based hyperspectral image sensors | | | Week 3 | Hyperspectral images and its application in atmosphere | | | Week 4 | Information extraction from HSI. | | | Week 5 | Optimum band extraction from HSI | | | Week 6 | Advanced algorithms in feature extraction from HSI | | | Week 7 | Using Multispec program and applying tutorial for dimensionality reduction. | | | Week 8 | Choosing best bands with dimensionality reduction methods | | | Week 9 | Midterm exam | | | Week 10 | Agricultural applications of HSI. | | | Week 11 | Geological applications of HSI | | | Week 12 | Forestry applications of HSI. | | | Week 13 | Atmospheric corrections of HSI in Envi | | | Week 14 | Classification of HSI in ENVI | | | Week 15 | Feature extraction from HSI in ENVi | | | Week 16 | Final Exam | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | 7
11
2017 | | 30 | | Homework/Assignment/Term-paper | 10 | | | 20 | | End-of-term exam | 16 | 2
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2018 | | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | | | | |
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