CODE 104827 ACADEMIC YEAR 2022/2023 CREDITS 5 cfu anno 2 INTERNET AND MULTIMEDIA ENGINEERING 10378 (LM-27) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/02 LANGUAGE English TEACHING LOCATION GENOVA SEMESTER 2° Semester MODULES Questo insegnamento è un modulo di: IMAGE PROCESSING AND REMOTE SENSING 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 GABRIELE MOSER Ricevimento: By appointment ALESSANDRO FEDELI Ricevimento: By appointment. 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 Class schedule The timetable for this course is available here: Portale EasyAcademy 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 Data appello Orario Luogo Degree type Note 09/01/2023 10:00 GENOVA Orale 23/01/2023 10:00 GENOVA Orale 13/02/2023 10:00 GENOVA Orale 31/05/2023 10:00 GENOVA Orale 13/07/2023 10:00 GENOVA Orale 04/09/2023 10:00 GENOVA Orale