155RESE Remote Sensing: Porovnání verzí

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Verze z 8. 1. 2023, 18:11

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, spectral signatures. 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 and exercises

Up-to-date and complete information, see here - remote sensing lectures

Practical exercises: Ing. Eva Matoušková, Ph.D. (eva.matouskova -et- fsv.cvut.cz) Ing. Tomáš Bouček (tomas.boucek -et- fsv.cvut.cz)

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

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   '''short [https://usermap.cvut.cz/profile/571e4f47-beef-4103-9e97-ba9daccc3d1d?lang=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 and exercises
 * Up-to-date and complete information, see here - '''[https://lfgm.fsv.cvut.cz/vyuka.html remote sensing lectures]'''