CODE 104827 ACADEMIC YEAR 2025/2026 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 TEACHING MATERIALS AULAWEB 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; parametric models for the statistics of optical and radar data; probabilistic graphical models and structured output learning for the semantic segmentation of satellite imagery; supervised regression 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 teaching unit 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 teaching unit will also complement the student's knowledge of this discipline. In general terms, after the teaching unit, the student shall be familiar with specific topics of prominent interest in the Earth observation field. TEACHING METHODS Class lectures and laboratory exercises. Students with valid certifications for Specific Learning Disorders (SLDs), disabilities or other educational needs are invited to contact the teacher and the School's contact person for disability at the beginning of teaching to agree on possible teaching arrangements that, while respecting the teaching objectives, take into account individual learning patterns. Contacts of the teacher and the School's disability contact person can be found at the following link https://unige.it/en/commissioni/comitatoperlinclusionedeglistudenticondisabilita 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 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. TEACHERS AND EXAM BOARD ALESSANDRO FEDELI Ricevimento: By appointment. GABRIELE MOSER Ricevimento: By appointment. LESSONS LESSONS START https://corsi.unige.it/en/corsi/10553/studenti-orario Class schedule REMOTE SENSING AND SATELLITE IMAGES EXAMS EXAM DESCRIPTION Mandatory written examination, made of multiple-choice questions about the topics in the syllabus of the teaching unit, 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 teaching unit 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. FURTHER INFORMATION Ask the professor for other information not included in the teaching schedule. 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