ENV-540 / 4 credits

Teacher: Tuia Devis

Language: English


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, deep 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)

 

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

In the programs

  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Image processing for Earth observation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10    CM1112
10-11    
11-12    CM1112
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Friday, 9h - 11h: Lecture CM1112

Friday, 11h - 12h: Exercise, TP CM1112