LEARNING OUTCOMES

Remote Sensing — Based on the concepts ruling the generation and propagation of electromagnetic wave fields, the objective is to provide the students with basic knowledge about the fundamentals and basic definitions of remote sensing; passive remote sensing in the optical, microwaves, and infrared frequency bands; active remote sensing and radar imaging; instrumentation for remote sensing. Satellite Images — The objective is to provide the students with basic knowledge about past, current, and forthcoming space missions for Earth observation; computational methods for the display, the modeling, and the filtering of satellite imagery; change detection techniques for multitemporal data; and regression techniques for bio/geophysical parameter retrieval from remote sensing. In this framework, machine learning techniques rooted in the areas of ensemble learning, neural networks, and kernel machines will be discussed as well.

AIMS AND LEARNING OUTCOMES

Based on the concepts ruling the generation and propagation of electromagnetic wave fields, after the course the student shall have basic knowledge about the fundamentals and definitions of remote sensing; passive remote sensing in the optical, microwaves, and infrared frequency bands; active remote sensing and radar imaging; and instrumentation for remote sensing.
The student shall also know about models and techniques to operate with remote sensing imagery for statistical modeling, spatial-contextual classification, and supervised regression. The methodological bases of these techniques are framed within the image processing, pattern recognition, and machine learning disciplines.
In general terms, after the course, the student shall be familiar with specific topics of prominent interest in the Earth observation field.

TEACHING METHODS

Class lectures and laboratory exercises

SYLLABUS/CONTENT

- Basic concepts ruling the generation and propagation of electromagnetic waves and fields
- Fundamentals and definitions of remote sensing
- Passive remote sensing in the optical, microwaves, and infrared frequency bands
- Active remote sensing and radar imaging
- Instrumentation for remote sensing
- Space missions for Earth observation
- Statistical modeling of remote sensing imagery
- Spatial-contextual classification of remote sensing images through probabilistic graphical models
- Bio/geophysical parameter regression from remote sensing data through ensemble learning, kernel machines, and neural networks

RECOMMENDED READING/BIBLIOGRAPHY

• Bishop C., Pattern recognition and machine learning, Springer, 2006
• Campbell J. B. and Wynne R. H., Introduction to remote sensing, Guilford Press, 2011
• Elachi C., Van Zyl J., Introduction to the physics and techniques of remote sensing, Wiley, 2006
• Goodfellow I., Bengio Y., and Courville A., Deep learning, MIT Press, 2016
• Hastie T., Tibshirani R., and Friedman J., The elements of statistical learning, Springer, 2008
• Ulaby F. T. and Long D., Microwave radar and radiometric remote sensing, Artech House, 2015
• Manolakis D. G., Lockwood R. B., and Cooley T. W., Hyperspectral imaging remote sensing, Cambridge University Press, 2016
• Moser G., Analisi di immagini telerilevate per osservazione della Terra, ECIG, 2007
• Moser G. and Zerubia J. (eds.), Mathematical models for remote sensing image processing, Springer, 2018
• Sonka M., Hlavac V., Boyle R., Image processing, analysis, and machine vision, Cengage Learning, 2015
• Class slides will be provided to the students through AulaWeb.