GMBB201 Image Processing in Geomatics
Showing course contents for the educational year 2022 - 2023 .
Course responsible: Ivar Maalen-Johansen, Cecilie Rolstad Denby
Teachers: Ivar Maalen-Johansen
ECTS credits: 5
Faculty: Faculty of Science and Technology
Teaching language: EN, NO
Teaching exam periods:
The course starts in the June block. The course has teaching/evaluation in the June block.
Course frequency: Every year.
First time: Study year 2006-2007
Lectures: The geometric and radiometric properties of satellite imagery. Geometric rectification/geocoding. Single-image photogrametry with emphasis on SPOT scenes. Control of geometric accuracy. Special image enhancement techniques. Mosaic production. Classification methods with emphasis on unsupervised classification. Use of satellite images for vegetation mapping. Evaluation of classification results. Image matching and filtering.
Exercises: Geometric rectificataion. Measurement of terrain elevation in oblique scene. Image matching. Special image processing methods: Pan-sharpening and mosaicing. Unsupervised classification (clustering) and field control.
Excursion: Field work in the Oslo-area.
Have knowledge of the most central ideas connected with the types of digital image processing that are relevant in geomatics, as well as be able to carry out such types of image processing, interpretation and analysis using a selected image processing tool (currently ERDAS Imagine). Through doing the compulsory exercises, the students are to become skilled in working in small, efficient groups. Through the writing of exercise reports, the students are to have gained skills in relevant presentation techniques through the use of suitable software. When using satelite pictures, the students are to see the opportunities and limitations that satelite images have as an integrated part of geographical information systems (GIS), used in connection with landscape planning, natural management and environmental monitoring . Have completed a field survey that forms the basis for the ability to assign information classes to spectral classes resulting from unsupervised classification. Have knowledge about and an understanding of image processing methods that are used for automatic measurement techniques in images.
Lectures, lab exercise and field exercise.
Learning support will primarily be given in connection with the part of the structured time that is allocated to exercise supervision. It will also be possible to make appointments with the course teacher within office hours.
Excerpts from selected textbooks and other publications.
Mandatory exercises and excursions.
Special requirements in Science
Type of course:
Lectures: 30 hours. Exercises: 40 hours. Excursion/field exercise: 8 hours.
Minimum 5 students
The external and internal examiner jointly prepare the exam questions and the correction manual. The external examiner reviews the internal examiner's examination results by correcting a random sample of candidate¿s exams as a calibration according to the Department's guidelines for examination markings.
Examination details: One written exam: Letter grades