155RESE Remote Sensing: Porovnání verzí

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  '''detailed [https://geo.fsv.cvut.cz/gwiki/Prof._Dr._Ing._Karel_Pavelka/en/ prof.Dr.Ing.Karel Pavelka]'''
  '''detailed [https://geo.fsv.cvut.cz/gwiki/Prof._Dr._Ing._Karel_Pavelka/en/ prof.Dr.Ing.Karel Pavelka]'''
Lectures
Lectures
* Up-to-date and complete information - '''[https://lfgm.fsv.cvut.cz/vyuka.html remote sensing lectures]'''
* Up-to-date and complete information, see here - '''[https://lfgm.fsv.cvut.cz/vyuka.html remote sensing lectures]'''





Verze z 8. 1. 2023, 17:37

Basic Information

  • 2 hours lectures per week
  • 2 hours exercises per week
  • 6 credits
  • finished with an exam
  • winter semester

Anotation

This lecture 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


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