Image processing for Earth observation
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:
- Basic concepts of remote sensing and digital imaging
- Platforms and sensors
- Information extraction, filtering, visual information
- Image classification, with machine and deep learning algorithms
- 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?
- Precis de Télédétection Volume 3 / Caloz
- Remote Sensing Image Processing, Morgan and Claypool / Camps-Valls
Ressources en bibliothèque
Websites
- http://www.oneonta.edu/faculty/baumanpr/geosat2/RS-Introduction/RS-Introduction.html
- http://www.crisp.nus.edu.sg/~research/tutorial/process.htm
- http://earthexplorer.usgs.gov/
- https://scihub.copernicus.eu/dhus/
- http://apps.sentinel-hub.com/eo-browser
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
- 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
- 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
- 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