Main Page     Information on the Institution     Degree Programs     General Information for Students     Türkçe  

 DEGREE PROGRAMS


 Associate's Degree (Short Cycle)


 Bachelor’s Degree (First Cycle)


 Master’s Degree (Second Cycle)

  Course Description
Course Name : Time Series Analysis and Change Detection Using Remote Sensing and GIS

Course Code : PM-587

Course Type : Optional

Level of Course : Second Cycle

Year of Study : 1

Course Semester : Fall (16 Weeks)

ECTS : 6

Name of Lecturer(s) : Prof.Dr. HAKAN ALPHAN

Learning Outcomes of the Course : understands change detection based on digital image processing on the basic level
decides the correct procedures about image processing prior to operations when necessary
learns change detection methods based on digital image processing
decides appropriate analysis approaches to produce digital data which is required to deal with the problem
expresses change information through maps and statistics

Mode of Delivery : Face-to-Face

Prerequisites and Co-Prerequisites : None

Recommended Optional Programme Components : None

Aim(s) of Course : aims to present information about visual analysis methods by using the changes in the landscape image algebra, conversion, classification, GIS and implementation of these methods to urban areas, forests and coastal areas by using various algorithms, methods and approaches in terms of their use, advantages and disadvantages, and the need for resource management.

Course Contents : Introduction to digital image processing for change detection.Pre-processing requirements for change detection. their significnce level ,Image algebra methods: Binary change detection, comparing spectral bands, labeling change detection results.Image algebra methods: Change detection using vegetation indices such as NDVI, SAVI, MSAVI and indices for built-up environment such as NDBI.Image transformation methods, Principal components analysis (PCA), Kauth-Thomas (Tasseled Cap) and Gramm-Schmidt transformations.Transforming bi-temporal and multitemporal data.Classification method: post-classification comparison, spectral and temporal mixture analysis, expectation maximization, unsupervised classification, etc.Advanced methods of change detection.GIS and other analysis methods.Change detection for forest, urban, agriculture and wetland areas.Selecting appropriate change detection procedure for a specific problem.

Language of Instruction : Turkish

Work Place : Classroom and Lab


  Course Outline /Schedule (Weekly) Planned Learning Activities
Week Subject Student's Preliminary Work Learning Activities and Teaching Methods
1 Introduction to digital image processing for change detection. Announging scopes of micro-projects and formation of project groups Supplementary course material , and related parts Oral and multimedia presentations and discussions
2 Pre-processing requirements for change detection, their significance level, overview and classification of change detection methods Supplementary course material , and related parts Oral and multimedia presentations and discussions
3 Image algebra methods: Image differencing, image ratioing,image regression, and change vector analysis Supplementary course material , and related parts Oral and multimedia presentations and discussions
4 Image algebra methods: Binary change detection, and, labeling change detection Supplementary course material , and related parts Oral and multimedia presentations and discussions
5 Image algebra methods: Change detection using vegetation indices such as NDBI made by using NDVI, SAVI, MSAVI Supplementary course material , and related parts Oral and multimedia presentations and discussions
6 Image transformation methods, Principal components analysis (PCA), Kauth-Thomas (Tasseled Cap) and Gramm-Schmidt transformations Supplementary course material , and related parts Oral and multimedia presentations and discussions
7 Transforming bi-temporal and multitemporal data Supplementary course material , and related parts Oral and multimedia presentations and discussions
8 Classification method: post-classification comparison, spectral and temporal mixture analysis, expectation maximization, unsupervised classification, etc. Supplementary course material , and related parts Oral and multimedia presentations and discussions
9 Mid-term exam Supplementary course material , and related parts Written Examination
10 Advanced methods of change detection Supplementary course material , and related parts Oral and multimedia presentations and discussions
11 GIS and other analysis methods Supplementary course material , and related parts Oral and multimedia presentations and discussions
12 Change detection for forest, urban, agriculture and wetland areas Supplementary course material , and related parts Oral and multimedia presentations and discussions
13 Advantages and disadvantages of selecting appropriate change detection procedure , determiners and constraints in fictionalization of ideal change detection Supplementary course material , and related parts Oral and multimedia presentations and discussions
14 Project presentations Supplementary course material , and related parts Oral and multimedia presentations and discussions
15 Project presentations Supplementary course material , and related parts Oral and multimedia presentations and discussions
16/17 Final Exam Supplementary course material , and related parts Written Examination


  Required Course Resources
Resource Type Resource Name
Recommended Course Material(s)  
 
 
Required Course Material(s)  


  Assessment Methods and Assessment Criteria
Semester/Year Assessments Number Contribution Percentage
    Mid-term Exams (Written, Oral, etc.) 1 70
    Homeworks/Projects/Others 1 30
Total 100
Rate of Semester/Year Assessments to Success 40
 
Final Assessments 100
Rate of Final Assessments to Success 60
Total 100

  Contribution of the Course to Key Learning Outcomes
# Key Learning Outcome Contribution*
1 Develops and increases the level of expertise in the same or different fields, using undergraduate knowledge and competency 5
2 Understands the interdisciplinary interaction related to profession. 4
3 Has advanced knowledge and understanding based on the basis of competencies gained at the undergraduate level to conduct research in the field of landscape design and landscape planning 5
4 Has critical awareness in the field of landscape planning and landscape design in terms of the nature and resources of knowledge as well as producing and testing knowledge 4
5 Has the ability to use the theoretical and practical knowledge gained in the field. 5
6 Creates new knowledge skillfully by integrating the knowledge gained in the field of landscaping with information from different disciplines; tackles with the problems which require expertise by using scientific reseach methods 5
7 Has cognitive and practical skills which is required to gain competency in professional practice 4
8 Applies the acquired knowledge, understanding and problem-solving skills in new but unusual environments as well as in broader, interdisciplinary and multidisciplinary contexts. 4
9 Tackles with problem, develops a solution method, evaluates the results and applies when necessary. 5
10 Takes responsibility to develop new strategical approaches and to create solutions in complex and unpredictable circumstances 5
11 Evaluates information related to the field critically, directs learning and conducts advanced studies independently 4
12 Presentswritten or orally his own studies or current developments in the field to people in or out of the field using visuals 4
13 Examines social relations and the norms that direct these relations critically and develops the situation when necessary 5
14 Has a good command of a foreign language, at least at B2 level of European Language Portfolio, to communicate orally or written 4
15 Uses advanced information and communication technologies with the required level by computer softwares. 5
16 Develops strategies, policies and implementation plans related to landscape planning and landscape design and evaluates the obtained results within the framework of quality processes 5
17 Collects data about landscape planning and landscape design and makes interpretation, teaches or announces the acquired knowledge in line with social, scientific and ethical values 5
18 Uses the knowledge of landscape planning and landscape design practice and problem solving skills in interdisciplinary studies 5
* Contribution levels are between 0 (not) and 5 (maximum).

  Student Workload - ECTS
Works Number Time (Hour) Total Workload (Hour)
Course Related Works
    Class Time (Exam weeks are excluded) 14 3 42
    Out of Class Study (Preliminary Work, Practice) 14 6 84
Assesment Related Works
    Homeworks, Projects, Others 1 14 14
    Mid-term Exams (Written, Oral, etc.) 1 1 1
    Final Exam 1 1 1
Total Workload: 142
Total Workload / 25 (h): 5.68
ECTS Credit: 6