ENV-540 / 3 crédits

Enseignant: de Morsier Frank Grégoire Jean

Langue: Anglais


Summary

This course covers optical remote sensing from satellites and airborne platforms. The different systems are presented. The students will acquire skills in image processing and machine learning to extract end-products, such as land cover or risk maps, from the images.

Content

Keywords

Imagery, remote sensing, image processing, signal processing, machine learning, satellites

Learning Outcomes

By the end of the course, the student must be able to:

  • Describe remote sensing systems
  • Describe applications of remote sensing
  • Select appropriately the relevant system for a given application
  • Perform image classification
  • Perform information extraction
  • Implement a processing chain to solve a real problem

Transversal skills

  • Use a work methodology appropriate to the task.
  • Continue to work through difficulties or initial failure to find optimal solutions.
  • Access and evaluate appropriate sources of information.
  • Collect data.
  • Make an oral presentation.
  • Write a scientific or technical report.
  • Assess progress against the plan, and adapt the plan as appropriate.
  • Use both general and domain specific IT resources and tools

Teaching methods

Lessons ex-cathédra (2/3)

Exercise sessions and group project (1/3)

Assessment methods

  • Mid-term written test (40% of the final mark)
  • Project report (60% of the final mark)

 

remark: the balance between mid-term and project has changed since previous years.

Resources

Bibliography

  • R. Caloz, C. Collet, Precis de Télédétection Volume 3: Traitements numériques d¿images de télédétection, Presses Universitaires du Québec
  • G. Camps-Valls, D. Tuia, L. Gomez-Chova, S. Jmenez, J. Malo, Remote Sensing Image Processing, Morgan and Claypool, available (on EPFL site) http://www.morganclaypool.com/doi/abs/10.2200/S00392ED1V01Y201107IVM012

Ressources en bibliothèque?



Ressources en bibliothèque

Websites

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Image processing for Earth observation
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Image processing for Earth observation
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Image processing for Earth observation
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Image processing for Earth observation
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Image processing for Earth observation
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22