ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE

Syllabus ( GEOD 523 )


   Basic information
Course title: Computer Vision and Image Processing
Course code: GEOD 523
Lecturer: Prof. Dr. Taşkın KAVZOĞLU
ECTS credits: 7.5
GTU credits: 3 (3+0+0)
Year, Semester: 1/2, Fall and Spring
Level of course: Second Cycle (Master's)
Type of course: Area Elective
Language of instruction: Turkish
Mode of delivery: Face to face
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: The aim of this course is to teach students how to apply digital image processing techniques to remotely sensed images, how to make raw remote sensing data useful by applying required pre-processing algorithms, which digital image processing technique should be used for a particular remote sensing problem, how to use a well-known software for digital image processing techniques and apply them to solve remote sensing problems.
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Apply digital image processing techniques to remotely sensed images

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Geodesy and Photogrammetry Engineering
    2. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields
    3. Operate modern equipments and hardwares, and use related technical skills in the field of Geodesy and Photogrammetry Engineering.

    Method of assessment

    1. Homework assignment
  2. Use the most appropriate image processing technique for different data sets and problems

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Geodesy and Photogrammetry Engineering
    2. Formulate and solve advanced engineering problems
    3. Operate modern equipments and hardwares, and use related technical skills in the field of Geodesy and Photogrammetry Engineering.

    Method of assessment

    1. Written exam
  3. Apply required corrections for raw remotely sensed imagery

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Geodesy and Photogrammetry Engineering
    2. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results

    Method of assessment

    1. Written exam
   Contents Up
Week 1: Introduction to digital image processing
Week 2: Radiometric correction techniques applied to satellite images
Week 3: Atmospheric correction methods and their application to images, sample applications.
Week 4: Fundamentals of geometric rectification or image restoration, polynomial approaches and resampling methods, sample applications in the laboratory.
Week 5: Techniques used in contrast enhancement of images, other image enhancement techniques including IHS transformation Fourier transformation, Principal Components Analysis etc. Assignment - 1.
Week 6: Histogram equalization, density slicing, pseudocolor transformation, application of all enhancement methods and comparison of the results
Week 7: Midterm exam
Week 8: Basics of image analysis techniques, image arithmetics and its possible applications, other operational processes on images.
Week 9: RGB to IHS and IHS to RGB transformations, when to use, its advantages, sample applications in laboratory.
Week 10: PVI, NDVI and PCA techniques, applications and analysis of the results.Assignment - 2.
Week 11: Spatial and frequency filters applied to remotely sensed image data, filtering techniques in remote sensing (low-pass, high-pass)
Week 12: Digital image classification, theory and methods, supervised and unsupervised classification techniques.
Week 13: Applications on sample data sets, preprocesing of radar images, filtering of noises (speckle) in images
Week 14: Basics of image fusion, optic and radar image fusion, pansharpening process and related methods, analysis of hiperspectral images (sample data from DAIS 7915)
Week 15*: .
Week 16*: Final exam
Textbooks and materials:
Recommended readings: Mather, P. M., 1999, Computer processing of remotely-sensed images: an introduction: Chichester, John Wiley.
Jensen, J. R., 1996, Introductory digital image processing - a remote sensing perspective: London, Prentice Hall.
Lillesand, T. M., and R. W. Kiefer, 1994, Remote sensing and image interpretation: New York, John Wiley & Sons.
Campbell, J. B., 1987, Introduction to remote sensing: London, The Guilford Press.
Maktav, D. ve Sunar, F., 1991, Uzaktan algılama, kantitatif yaklaşım: İstanbul, Hürriyet Ofset A.Ş.
Barrett, E.C., and Curtis, L.F., 1999, An introduction to environmental remote sensing, Routledge.
Congalton, R.G., and Green, K., 1998, Assessing the accuracy of remotely sensed data: principles and practices, Lewis Publishers.
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 7 30
Other in-term studies: 0
Project: 0
Homework: 5,10 30
Quiz: 0
Final exam: 16 40
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 2 14
Practice, Recitation: 0 0
Homework: 10 10
Term project: 0 0
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 5 1
Mid-term: 1 1
Personal studies for final exam: 10 1
Final exam: 2 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
-->