ENV-540 / 4 crédits

Enseignant: Tuia Devis

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/deep learning to extract end-products, such as land cover or risk maps, from the images.

Content

Courses content:

  1. Basic concepts of remote sensing and digital imaging
  2. Platforms and sensors
  3. Information extraction, filtering, visual information
  4. Image classification, with machine and deep learning algorithms
  5. Project: study a real problematic using remote sensing and image processing techniques.

Keywords

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

Learning Prerequisites

Recommended courses

Machine learning CS-433

Important concepts to start the course

Intermediate skills in Python programming are considered a pre-requisite. All the exercises will be in Python.

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)

Expected student activities

- Following classes

- exercises (individual or in small groups)

- preparing presentations

- reading club or research papers

- final projects in small groups

Assessment methods

  • Mid-term written test (50% of the final mark)
  • Project report (50% of the final mark)

 

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

Moodle Link

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
  • Type: optionnel
  • 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
  • Type: optionnel
  • 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
  • Type: optionnel
  • 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
  • Type: optionnel
  • 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
  • Type: optionnel
  • 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
  • Type: optionnel
  • 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
  • Type: optionnel
  • 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
  • Type: optionnel
  • 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
  • Type: optionnel

Semaine de référence

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