Introduction to interactive, semi-automatic and automatic methods for image interpretation and data analysis; methodological aspects of computer-assisted remote sensing, including semantic image classification and segmentation; detection and extraction of individual objects; estimation of physical parameters.
Learning objective
Understanding the tasks, problems, and applications of image interpretation; basic introduction of computational methods for image-based classification and parameter estimation (clustering, classification, regression), with focus on remote sensing.
Content
Image (and point-cloud) interpretation tasks: semantic classification (e.g. land-cover mapping), physical parameter estimation (e.g. forest biomass); Image coding and features; probabilistic inference, generative and discriminative models; clustering and segmentation; continuous parameter estimation, regression; classification and labeling; deep learning; atmospheric influences in satellite remote sensing;
Literature
J. A. Richards: Remote Sensing Digital Image Analysis - An Introduction C. Bishop: Pattern Recognition and Machine Learning
Prerequisites / Notice
basics of probability theory and statistics; basics of image processing; elementary programming skills (Matlab);
Performance assessment
Performance assessment information (valid until the course unit is held again)
The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examination
oral 30 minutes
Additional information on mode of examination
The grade will be determined by (i) a 30 minutes oral exam and (ii) graded lab assignments submitted during the semester ("obligatorische Leistungselemente"). There is no separate pass/fail for the lab part, students can present themselves for the final exam irrespective of their lab grades. The final grade depends 70% on the exam and 30% on the assignments.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.
Learning materials
No public learning materials available.
Only public learning materials are listed.
Groups
No information on groups available.
Restrictions
There are no additional restrictions for the registration.