REMOTE SENSING AND SATELLITE IMAGES

REMOTE SENSING AND SATELLITE IMAGES

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Last update 15/09/2021 12:20
Code
104827
ACADEMIC YEAR
2021/2022
CREDITS
5 credits during the 2nd year of 10378 INTERNET AND MULTIMEDIA ENGINEERING (LM-27) GENOVA
SCIENTIFIC DISCIPLINARY SECTOR
ING-INF/02
LANGUAGE
English
TEACHING LOCATION
GENOVA (INTERNET AND MULTIMEDIA ENGINEERING)
semester
2° Semester
modules
This unit is a module of:
Teaching materials

OVERVIEW

In this course, basic concepts of remote sensing and of the analysis of the resulting imagery are discussed.

 

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, despeckling, spatial-contextual classification, multitemporal analysis, 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 (approximately 45 hours) and laboratory exercises (approximately 5 hours)

 

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, filtering, and despeckling of remote sensing imagery
- Spatial-contextual classification of remote sensing images through probabilistic graphical models
- Change detection with multitemporal remote sensing images
- 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
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.

TEACHERS AND EXAM BOARD

Ricevimento: By appointment

Ricevimento: By appointment.

LESSONS

Teaching methods

Class lectures (approximately 45 hours) and laboratory exercises (approximately 5 hours)

 

LESSONS START

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

Oral examination

 

ORARI

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.