Learning Goals

Students acquire in-depth knowledge of operational applications of remote sensing data. This includes working with optical and radar data. In particular, the seminar will focus on the classification of remote sensing data and will also deal with the evaluation of data and result quality.

Contents

• Understand the differences between supervised and unsupervised classification, as well as their possibilities and limitations.
• Understand the operational procedures of pixel-based classification (workflow planning) and the advantages/disadvantages of the different approaches
• Unsupervised classification: principle, procedure, results and their usability
• Supervised classification methods: Prerequisites = training areas: Selection and quality control, parametric and non-parametric classification methods according to the capabilities of available software including knowledge-based methods
• Accuracy assessment: result verification/accuracy assessment (users vs. producers accuracy)
• Special applications: Analysis of multi-temporal data sets; change detection and its main variants

Teaching methods

Seminar (introduction to theoretical concepts, practical implementation, project work). Methodological skills are deepened through practical exercises. Open-source software such as SNAP and ArcGIS Pro are used to process remote sensing data as part of classification procedures. 

Mode of assessment

Project-oriented term paper

Additional Information

Conditions for granting credit points

Successful completion by passing the examination task

Usage of the module
Mandatory module within the M.Sc. program Geography, specialization Geomatics; Elective module within the other M.Sc. specializations.

Stellenwert der Note für die Endnote
The module grade is CP-weighted (6/120) and is included in the final M.Sc. grade.