Information Package / Course Catalogue
Time Series Analysis and Change Detection Using Remote Sensing
Course Code: ZPM534
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

Aims to present information about visual analysis methods by using the changes in the landscape image algebra, conversion, classification, GIS by using various algorithms, methods and approaches in terms of their use, advantages and disadvantages, and the need for landscape management

Course Content

Pre-processing requirements for different change detection techniques. Introduction to digital image processing for change detection. Image algebra methods, Classification method: post-classification comparison, spectral and temporal mixture analysis, expectation maximization, unsupervised classification.Advantage and disadvantage of change detection procedure for a specific problem.

Name of Lecturer(s)
Learning Outcomes
1.Understands change detection based on digital image processing on the basic level
2.Decides the correct procedures about image processing prior to operations when necessary
3.Learns change detection methods based on digital image processing
4.Decides appropriate analysis approaches to produce digital data which is required to deal with the problem
5.Expresses change information through maps and statistics
Recommended or Required Reading
1.Alphan, H., (2004) “ Kıyı Alanları Yönetiminde Uzaktan Algılama Yöntemleri ile İzleme Programı.” Çukurova Üniversitesi Fen Bilimleri Enstitüsü Peyzaj Mimarlığı Anabilim Dalı, Doktora Tezi , Adana.
2.Campbell, J.B., 1996, Introduction to Remote Sensing, 2nd Edition, Guilford Press, Newyork.
3.CCRS, 1998, Canada Center of Remote Sensing. Fundamentals of Remote Sensing. http://www.ccrs.nrcan.gc.ca
4.Collins, J. B. and Woodcock, C. E., 1996, An assessment of several linear change detection techniques for mapping forest mortality using multitemporal Landsat TM data. Remote Sensing of Environment, 56, 66–77.
5.Jensen, J.R., 1996, Introductory Digital Image Processing: A Remote Sensing Perspective(2 nd eddition), Prentice-Hall, Inc., Upper Sandle River, NJ.
6.Lu, D., Mausel, P., Brondizio, E., Moran, E., 2003, Change Detection Techniques, International Journal of Remote Sensing, Vol. 25, No. 12, 2365- 407.
7.Mitri, G. H., Gitas, I. Z., 2004, “A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery”, International Journal of Remote Sensing, Vol. 27, No. 1, 4154.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to course: content, reason, importance, process method and needs.
Week 2 - Theoretical
Information about satellite images
Week 3 - Theoretical
Pre-processing techniques for change detection
Week 4 - Theoretical
Image differencing, image ratioing,image regression, and change vector analysis
Week 5 - Theoretical
Binary change detection and labeling change detection
Week 6 - Theoretical
Change detection using vegetation indices such as NDVI, SAVI, MSAVI
Week 7 - Theoretical
Transforming bi-temporal and multitemporal data
Week 8 - Intermediate Exam
Mid-term exam
Week 9 - Theoretical
Classification method: piksel based unsupervised classification, post-classification comparison
Week 10 - Theoretical
Classification method: piksel based supervised classification, post-classification comparison
Week 11 - Theoretical
Classification method: object based unsupervised classification, post-classification comparison
Week 12 - Theoretical
Classification method: object based supervised classification, post-classification comparison
Week 13 - Theoretical
Advantage and disadvantage of different change detection techniquis
Week 14 - Theoretical
Project presentations
Week 15 - Theoretical
Project presentations
Week 16 - Final Exam
Final exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory1483154
Midterm Examination120121
Final Examination124125
TOTAL WORKLOAD (hours)200
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
OÇ-1
3
5
5
5
5
OÇ-2
3
5
5
5
1
OÇ-3
3
5
5
5
1
OÇ-4
3
5
5
5
1
OÇ-5
3
5
5
5
1
Adnan Menderes University - Information Package / Course Catalogue