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103-0287-00L 4 Credits MSC D-BAUG
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Image Interpretation

Lecturers & Examiners: Prof. Dr. Konrad Schindler
VVZ CR n/a

Last Updated: 2026-02-05 15:34:51

Abstract

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.

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;

Resources

Literature

J. A. Richards: Remote Sensing Digital Image Analysis - An Introduction C. Bishop: Pattern Recognition and Machine Learning

General Information

Language
English
Levels
MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
oral 30 minutes
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.

Course Components

Type Title Time & Place Hours
lecture with exercise Image Interpretation
In Präsenz: 12. Nov, 19. Nov; HIL C71 (IGP Computerlabor); praktische Übung erfordert spezielle Computer-Hardware und Software.
  • Thu 08:50-11:30 (HIL D 53)
3 h weekly

Offered In