OVERVIEW

AIMS AND CONTENT

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, semantic segmentation, and supervised regression. The methodological bases of these techniques are framed within the discipline of machine learning and pattern recognition, hence the course will also complement the student's knowledge of this discipline.
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
- Semantic segmentation of remote sensing images through probabilistic graphical models
- Bio/geophysical parameter regression from remote sensing data through ensemble learning, kernel machines, and neural networks

This course also contributes to the achievement of the following Sustainable Development Goals of the UN 2030 Agenda: Objectives no. 4 and 8.

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
• Class slides will be provided to the students through AulaWeb.

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Prof. Federico Scarpa (federico.scarpa@unige.it), the disability liaison for the Engineering study programs.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/10378/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Mandatory written examination, made of multiple-choice questions about the topics in the syllabus of the course, with maximum admissible mark equal to 24/30. If a student obtains a sufficient mark in this written exam, then he/she can also optionally take an additional oral examination with maximum admissible mark equal to 30/30 with honors.

ASSESSMENT METHODS

Within the mandatory written examination, the student's knowledge of the main concepts discussed in the course shall be evaluated. Within the optional oral examination, the student's capability to address simple problems of remote sensing and of satellite image analysis and his/her capacity to critically discuss the related methodological bases shall be assessed.

Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

FURTHER INFORMATION

Students who have a valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework, and exams should speak both with the instructor and with Professor Federico Scarpa (federico.scarpa@unige.it ), the School's disability liaison.