Processing of Remotely sensed images

Introduction


This course aims to provide every student with a working knowledge of sophisticated methods and techniques for processing and analyzing remotely sensed data; as well as the theory and practice of undertaking remote sensing image analysis. Throughout the course, emphasis will be placed on image processing, image analysis, image classification, and other advances classification techniques such as fuzzy approaches, textural classification and object-based methods. The course is composed of lectures, laboratory exercises and a term project.

 


Learning outcomes


After the course, students should be able to understand sophisticated techniques for applying
advanced methods for image processing and analysis, and to use remotely sensed data for various applications such as planning, environmental monitoring and natural resource management.

 


Content


The course is composed of lectures, laboratory exercises and a term project. During laboratory sessions, you will have the opportunity to improve your skills on digital image processing and analysis, as well as to conduct a remote sensing project. All meetings of the lab groups are held weekly using the well-known Image processing software such as PCI Geomatica or ENVI.
Students who work two in a group should submit one group report for each lab. The course content includes:


  • Image Processing
  • Image Analysis
  • Image Classification
  • Fuzzy classification
  • Object-based classification approaches
  • Using texture in classification
  • Classification uncertainty
  • Image fusion techniques
  • Image matching
  • Dense matching

 

Allocated time per teaching and learning method

 

Teaching / learning method

Allocated Hours

Lectures

36

Supervised practicals

40

Unsupervised practicals

10

Individual assignment

24

Group assignment

0

Self-study

30

Examination

4

Excursion

0

Fieldwork

0

Graduation project supervision

0

MSc thesis supervision

0

Development time

0

 


Examination


There will be a midterm and a final examination. All examinations will be closed book and closed notes. In addition, students will be given some Term Project related to working with different image classification techniques, how the texture can improve the classification and comparing some image fusion approaches.

 

 

Final Grade Determination

 

Components

Weight

Assignments

20 %

Midterm Examination

30 %

Term Project

10 %

Final Examination

40 %



Reference


Paul M. Mather, Magaly Koch, “Computer Processing of Remotely-Sensed Images: An Introduction”, 4th Edition.

 

 

Date:
2018/06/02
review:
72
K.N.Toosi University
Address: No. 1346, ValiAsr Street, Mirdamad cross, Geomatics Engineering Faculty, K.N. Toosi University of Technology, Tehran, Iran.
Post Code: 15433-19967 
 Tel: +98 21 88877071-2 
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