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 from the images such as land cover or risk maps.

Content

Keywords

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

Learning Prerequisites

Recommended courses

Machine learning CS-433 or equivalent (e.g. CIVIL-226).

Important concepts to start the course

Intermediate skills in Python programming are considered a pre-requisite. All the exercises will be in Python. Basic knowledge in Pytorch is required.

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
• Design an appropriate image processing pipeline to address a geospatial problem
• Analyze the results of a machine learning methods
• Implement an end to end machine learning pipeline
• Discuss the pros and cons of different remote sensing sensors

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
• Summarize an article or a technical report.

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

• Find the references at the Library

Websites

• 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

• https://go.epfl.ch/ENV-540