MHKD214 Remote sensingIstanbul Okan UniversityDegree Programs Survey And CadastreGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Survey And Cadastre
Associate TR-NQF-HE: Level 5 QF-EHEA: Short Cycle EQF-LLL: Level 5

General course introduction information

Course Code: MHKD214
Course Name: Remote sensing
Course Semester: Spring
Course Credits:
Theoretical Practical Credit ECTS
3 0 3 5
Language of instruction: TR
Course Requisites:
Does the Course Require Work Experience?: No
Type of course: Department/Faculty Elective
Course Level:
Associate TR-NQF-HE:5. Master`s Degree QF-EHEA:Short Cycle EQF-LLL:5. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Assoc. Prof. BİROL ALAS
Course Lecturer(s):
Course Assistants:

Course Objective and Content

Course Objectives: The concept of remote sensing and its uses, types of imaging, basics of remote sensing, electromagnetic radiation, spectrum, interaction of energy with atmospheres and objects, basic process steps of image processing in remote sensing, classification and information extraction.
Course Content: Basic concepts, Data and image types in remote sensing, Radar, SAR, IfSAR concepts, Lidar technology, Geometric corrections in satellite images, Image enrichment, histogram and positional filters, Analysis of classification accuracy

Learning Outcomes

The students who have succeeded in this course;
Learning Outcomes
1 - Knowledge
Theoretical - Conceptual
1) To understand the basics of remote sensing
2 - Skills
Cognitive - Practical
1) Comprehend the distinctions and distinctions of remote sensing with photogrammetry and GIS
3 - Competences
Communication and Social Competence
Learning Competence
1) Understanding image processing steps in remote sensing
Field Specific Competence
1) To understand the importance of image classification and information extraction
2) To understand active imaging systems and especially Lidar technology, to follow developments in this subject
3) Evaluate the possibilities of remote sensing to provide inventory information to land management
Competence to Work Independently and Take Responsibility

Lesson Plan

Week Subject Related Preparation
1) Introduction to remote sensing, places where it is used, basic concepts Lecture Notes
2) Types of remote sensing, differences according to each other Lecture Notes
3) EMR, spectrum, interaction of the order with the atmosphere and objects Lecture Notes
4) Data and image types, resolutions in remote sensing, image bands with Erdas Imagine Lecture Notes
5) Radar, SAR, IfSAR concepts, Lidar technology Lecture Notes
6) 4 basic processing steps in image processing, radiometric corrections Lecture Notes
7) The application of atmospheric correction with ATCOR software and the importance of atmospheric correction Lecture Notes
8) Midterm Exam none
9) Geometric corrections in satellite images, application with Erdas Imagine software Lecture Notes
10) Image enrichment, histogram and positional filters Lecture Notes
11) Objectives of image conversion functions, NDVI and PCA Lecture Notes
12) Applications with supervised and unsupervised classification and Erdas Imagine Lecture Notes
13) Analysis of classification accuracy Lecture Notes
14) Object-based image classification and application with Erdas Imagine Lecture Notes
15) Final Exam none
16) Final Exam none

Sources

Course Notes / Textbooks: Ders Notları
References: Fundamentals of Satellite Remote Sensing, Emilio Chuvieco, Alfredo Huete, Taylor & Francis, 2009
Introduction to Remote Sensing, James B. Campbell, Randolph H. Wynne, Fifth Edition, The Guilford Press

Course-Program Learning Outcome Relationship

Learning Outcomes

1

2

3

4

5

6

Program Outcomes
1) He/she knows main topics about profession with also has knowledge and awareness about social resposibility, ethical values and princibles of profession.
2) He/she has knowledge about basic mathematichal computing and problem solving about the issues of profession.
3) He/ she has knowledge about algorithm, basic programming, data structures and types, database management systems spatial database for operation of the software which are required by profession.
4) He/she has knowledge about all measurement, calculation, and exercising in the fields of survey and cadastre.
5) He/she has knowledge about legislation, ability of follow the changes and has command of the process in the fields of survey and cadastre.
6) He/she has ability to use technological tools and programs, know working and scientific principles of these technology that existed in profession area
7) He/she knows main topics, expertising, legislation and urban reneval issues about real estate.
8) He/she has command of application all kinds of engineering project to area.

Course - Learning Outcome Relationship

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) He/she knows main topics about profession with also has knowledge and awareness about social resposibility, ethical values and princibles of profession.
2) He/she has knowledge about basic mathematichal computing and problem solving about the issues of profession.
3) He/ she has knowledge about algorithm, basic programming, data structures and types, database management systems spatial database for operation of the software which are required by profession.
4) He/she has knowledge about all measurement, calculation, and exercising in the fields of survey and cadastre.
5) He/she has knowledge about legislation, ability of follow the changes and has command of the process in the fields of survey and cadastre.
6) He/she has ability to use technological tools and programs, know working and scientific principles of these technology that existed in profession area
7) He/she knows main topics, expertising, legislation and urban reneval issues about real estate.
8) He/she has command of application all kinds of engineering project to area.

Learning Activity and Teaching Methods

Field Study
Expression
Brainstorming/ Six tihnking hats
Individual study and homework
Lesson
Reading
Homework
Problem Solving
Q&A / Discussion

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Oral Examination
Homework
Individual Project

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Midterms 1 % 40
Final 1 % 60
total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
total % 100

Workload and ECTS Credit Grading

Activities Number of Activities Workload
Course Hours 14 42
Study Hours Out of Class 10 30
Homework Assignments 11 33
Midterms 2 12
Final 3 18
Total Workload 135