Digital Image Processing and Applications

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Course Description 

This is an advanced remote sensing course on sophisticated methods and techniques for collecting, processing and analyzing remotely sensed data; as well as applications of remote sensing in urban planning, environmental monitoring and natural resource management. Throughout the course, emphasis will be placed on image processing, image analysis, image classification, remote sensing and GIS data integration, and applications of remote sensing for monitoring global environmental changes to support sustainable development.

The course is composed of lectures, laboratory exercises and student presentations.

The intended learning outcomes

Students will gain theoretical knowledge and practical skills on digital image processing, analysis, and applying these techniques for mapping land cover/landuse and monitoring their changing patterns. 

Course Main Content
Earth Observation & In Situ Data
Image Processing, Analysis and Classification
EO Big Data Analytics with Google Earth Engine
Digital Change Detection
Remote Sensing Applications

Laboratory Sessions 

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 PCI Geomatica image processing and analysis software. Students who work in a group (Max. two students) should submit one group report for each lab/project. 


Student Presentations 
One of the requirements for this course is to present a remote sensing project to the class. More details will be published soon. 

Evaluation 
Exam grading: A-F (3 ECTS)

Lab grading: Pass or redo (3 ECTS)

Project grading: Pass or redo (1.5 ECTS)


Please note: 
1. All labs have a deadline. If not specified differently in the lab instructions, the due date is always one week after the lab session. Labs are to be uploaded to the Bilda system before the deadline to not forfeit the chance for bonus points. 6 bonus points (1 for each lab) towards the the final grade will be given to students who submit their lab reports and project reports on time.
2. Please observe KTH’s guidelines on academic honesty.

Teachers

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