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REMOTE SENSING AND SATELLITE IMAGES

CODE 104827
ACADEMIC YEAR 2022/2023
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, 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.

    TEACHERS AND EXAM BOARD

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Oral examination on the topics included in the syllabus of the course.

    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.

    ASSESSMENT METHODS

    Within the oral examination, the student's knowledge of the course topics and his/her capability to discuss how to address simple problems of remote sensing and of satellite image analysis shall be evaluated.

    Exam schedule

    Date Time Location Type Notes