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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 15:47:42

Abstract

Application of machine learning in satellite-based Earth observation; methodological and practical aspects of remote sensing data analysis, including atmospheric correction, image feature extraction, image classification and segmentation, regression of physical parameters

Objective

Learn how to apply image analysis and machine learning to image interpretation tasks in remote sensing; hands-on experience in implementing automatic image analysis methods, and in judging their results.

Content

Preprocessing of satellite images, atmospheric correction; extraction of features (radiometric indices, texture descriptors, etc.) from raw image intensities; semantic image segmentation (e.g., cloud masking); physical parameter estimation (e.g., vegetation height); practical deployment of classical machine learning algorithms as well as deep neural networks for remote sensing data analysis; assessment of prediction results

General Information

Language
English
Levels
MSC
Frequency
Yearly recurring

Examination

Type
graded semester performance
During the course, student groups will implement several image analysis tasks, and present their projects in class. The grade will be determined by the submitted assignments and the associated presentations.

Course Components

Type Title Time & Place Hours
lecture with exercise Image Interpretation
  • Thu 08:50-11:30 (HIL D 53)
3 h weekly

Offered In