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REMOTE SENSING AND SATELLITE IMAGES

CODE 104827
ACADEMIC YEAR 2021/2022
CREDITS
  • 10 cfu during the 2nd year of 10378 INTERNET AND MULTIMEDIA ENGINEERING(LM-27) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR ING-INF/02
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 2° Semester
    MODULES This unit is a module of:
    TEACHING MATERIALS AULAWEB

    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

    Exam Board

    SILVANA DELLEPIANE (President)

    FEDERICA FERRARO

    MATTEO PASTORINO

    ANDREA RANDAZZO

    SEBASTIANO SERPICO

    ALESSANDRO FEDELI (President Substitute)

    GABRIELE MOSER (President Substitute)

    LESSONS

    LESSONS START

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

    Oral examination

     

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    Exam schedule

    Date Time Location Type Notes
    10/01/2022 10:00 GENOVA Orale
    24/01/2022 10:00 GENOVA Orale
    14/02/2022 10:00 GENOVA Orale
    01/06/2022 10:00 GENOVA Orale
    16/06/2022 10:00 GENOVA Orale
    05/07/2022 10:00 GENOVA Orale
    25/07/2022 10:00 GENOVA Orale
    05/09/2022 10:00 GENOVA Orale