Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Institute of Graduate Programmes
  • Remote Sensing and Geographical Information Syst.
  • Program in Remote Sensing and Geographical Information Systems (Distance Learning)
  • Course Structure Diagram with Credits
  • Identification of Field Components by Spectral Band Analysis
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title UCS598 - Identification of Field Components by Spectral Band Analysis
Course Type Elective Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ METİN ALTAN
Mode of Delivery Face to Face
Prerequisites There are no prerequisites for this course.
Courses Recomended There are no other courses recommended as support or alternatives for this course.
Required or Recommended Resources Marcus Borengasser, William S. Hungate, Russell Watkins - "Hyperspectral Remote Sensing: Principles and Applications"
Recommended Reading List Marcus Borengasser, William S. Hungate, Russell Watkins - "Hyperspectral Remote Sensing: Principles and Applications"
Assessment methods and criteria A midterm exam, a final exam
Work Placement No internship will be required.
Sustainability Development Goals Industry, Innovation and Infrastructure , Climate Action , Terrestrial Life

Content

Weeks Topics
Week - 1 Foundations of Spectral Analysis and Field Spectroscopy
Week - 2 Operating Principles of Spectrometers and Calibration
Week - 3 Physical and Chemical Basis of Spectral Signatures
Week - 4 Spectral Sampling Strategies and Field Measurement Techniques
Week - 5 Spectral Libraries and Standard Data Formats
Week - 6 Spectral Data Pre-processing and Noise Removal Methods
Week - 7 Spectral Behavior of Vegetation and Index Analyses
Week - 8 Soil and Mineral Spectroscopy and Component Identification
Week - 9 Spectral Characterization of Water and Wetlands
Week - 10 Hyperspectral Band Analyses and Dimensionality Reduction
Week - 11 Spectral Unmixing Analysis
Week - 12 Spectral Matching Algorithms and Classification
Week - 13 Integration of Field Data with Satellite Imagery
Week - 14 Advanced Applications in Identification of Field Components

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Demonstration
  • Case Study
  • Problem Solving
  • Brain Storming
  • Competences
  • Productive
  • Rational
  • Questoning
  • Entrepreneur
  • Creative
  • Effective use of a foreign language
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Information Management

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
2.Midterm Exam 1 40
Final Exam 1 60
Toplam (%) 100
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