Earth Observation Lab in Teaching

Most of our students belong to the Bachelor’s and Master’s programs in Aerospace Engineering. In addition, we address students of the Computer Science Master’s program, who specialize in Geoinformatics. We educate undergraduate students in the basics of remote sensing, and teach all different aspects of Earth observation, from radar and laser techniques to optical remote sensing to graduate students.

Lectures offered in Bachelor's and Master's Degrees

Einführung in die Fernerkundung (B.Sc.)

368 Einführung in die Fernerkundung

  • Einordnung der Fernerkundung mit aktiven und passiven Sensoren in die Luft- und Raumfahrttechnik
  • Einführung in die verschiedenen Spektralbereiche (Optik, IR, Mikrowelle)
  • Strahlungsverhalten der Materie in den einzelnen Spektralbereichen
  • Aufbau von Fernerkundungssensoren und -systemen in den verschiedenen Spektralbereichen
  • Performance und Bildqualitätsparameter für die einzelnen Spektralbereiche
  • Zivile und militärische Anwendungen
  • Vorstellung operationeller und kommerzieller Softwarepakete
  • Analyse von Bildmaterial


Erdbeobachtung (M.Sc.)

1055 Earth observation

optical remote sensing
  • Optical image generation and recording
  • Basics of photogrammetry
  • Evaluation of spectral information
  • Country cover classifications
Radar and laser methods
  • Basics of radar technology
  • Synthetic aperture principle
  • Characteristics of SAR images
  • Principles of laser scanning
Interferometric SAR methods
  • SAR interferometry (InSAR)
  • Differential InSAR (DInSAR)
  • Persistent scatterer interferometry
  • Advanced SAR methods

2500 Image and signal processing for earth observation

Basics of signal processing
  • Discrete and continuous signals
  • Fourier transform and spectrum
  • Convolution and correlation
  • Basic filters
Image processing
  • Digital representation of images
  • Histograms and statistical moments
  • Color spaces and models
  • Two-dimensional convolution
  • Basic 2D filters
  • Image gradients
  • Feature extraction
Machine learning
  • Types of Machine Learning (Supervised, Unsupervised)
  • Data annotation
  • Training and evaluation
  • Typical models
  • Artificial neural networks

2504 Apparatus internship using space  

The students decide independently and with consideration
of available places for one of the two sub-areas of space use: satellite navigation or earth observation
You will then carry out your technical internship with a topic from this area.
The internship includes the following work points based on real satellite data:
  • Introduction to suitable evaluation software (eg SNAP, QGIS, Python or Google Earth Engine)
  • Depending on the subproject, procurement of available satellite data from suitable sources
  • Development of the question for a suitable scenario (eg crises or natural disasters)
  • Evaluation of satellite data for the analytical description of the selected scenario
  • Documentation and presentation of the results


Module manual