155RESE Remote Sensing
Basic Information
- 2 hours lectures per week
- 2 hours exercises per week
- 6 credits
- finished with an exam
- winter semester
Anotation
The lectures focuse on an explanation of the physical principle on which remote sensing (RS) is based, a technical explanation of measurement methods, the behaviour of individual substances in response to interaction with different types of electromagnetic radiation, and the possibility of using RS for a range of applications. The lectures contain: introduction to RS. Basic physical and mathematical relations. Image creation. Detectors and sensors. Spectral properties of substances and land features. Image manipulation, histogram. Image enhancement, edge filters. Supervised and unsupervised classification, clusters, training sets. Practical use of RS. Examples of data.
Generaly, this course shows basics processing methods and use of remotely sensed data. Theoretical lectures provide basics in optics, mathematics, surveying and physics for full understanding of the theme. In practical lessons the theory turns to practice and students process their own data from Sentinel 2 satelite using an open source ESA SNAP software.
- Literature
Lillesand, T.M., Kiefer, R.W., Chipman, J.W.: Remote Sensing and Image Interpretation, 7th Ed., Wiley, 2007. ISBN: 978-1-118-34328-9
Canty, M.J.: Image Analysis, Clasification and Change Detection in Remote Sensing. CRC Taylot& Francis. 2007. ISBN: 0-8493-7251-8
Lectures
Lecturer: prof. Dr. Ing. Karel Pavelka
lecturer information
short prof.Dr.Ing.Karel Pavelka detailed prof.Dr.Ing.Karel Pavelka
Lectures
- Up-to-date and complete information, see here - remote sensing lectures
Practical exercises: Ing. Eva Matoušková, PhD. and Ing. Tomáš Bouček
Exercises
- Introduction to remote sensing
- Data downloading ad data sources, free data sources
- Working with remote sensing data 1
- Working with remote sensing data 2
- Image filtering and processing
- Vegetation indices
- Unsupervised classification
- Supervised classification
- Practical example - land cover, land use
- Classification accuracy
- Introduction to hyperspectral data
- Principal Component Analysis