155RESE Remote Sensing: Porovnání verzí
| Řádek 28: | Řádek 28: | ||
==Exercises== | ==Exercises== | ||
* Introduction | * Introduction to remote sensing | ||
* Data | * Data downloading ad data sources, free data sources | ||
* Working with remote sensing data 1 | * Working with remote sensing data 1 | ||
* Working with remote sensing data 2 | * Working with remote sensing data 2 | ||
* Image filtering and processing | * Image filtering and processing | ||
* Vegetation | * Vegetation indices | ||
* Unsupervised classification | * Unsupervised classification | ||
* Supervised classification | * Supervised classification | ||
* Practical example - land cover | * Practical example - land cover, land use | ||
* Classification accuracy | * Classification accuracy | ||
* Introduction to hyperspectral data | * Introduction to hyperspectral data | ||
* Principal Component Analysis | * Principal Component Analysis | ||
Verze z 5. 1. 2023, 13:51
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
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