CODE 90568 ACADEMIC YEAR 2018/2019 CREDITS 2.5 cfu anno 2 INTERNET AND MULTIMEDIA ENGINEERING 10378 (LM-27) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/03 TEACHING LOCATION GENOVA SEMESTER 2° Semester MODULES Questo insegnamento è un modulo di: REMOTE SENSING AND SATELLITE IMAGES TEACHING MATERIALS AULAWEB OVERVIEW In this course, basic concepts on the analysis of satellite images are discussed. The focus is on modeling and analysis methodologies that are peculiar of satellite remote sensing data rather than on general purpose image analysis notions. AIMS AND CONTENT LEARNING OUTCOMES 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 satellite data; and regression techniques for bio/geophysical parameter retrieval from remote sensing. AIMS AND LEARNING OUTCOMES After the course, the student shall know about models and methods to operate with satellite imagery for display, statistical modeling, denoising, despeckling, multitemporal analysis, and supervised regression purposes. The methodological bases of the course are framed within the image processing, pattern recognition, and machine learning disciplines. In this framework, after the course, the student shall be familiar with specific topics of prominent interest in the Earth observation field. TEACHING METHODS Class lectures (approximately 20 hours) and laboratory exercizes (approximately 5 hours) SYLLABUS/CONTENT Space missions for Earth observation and their applications Methods for satellite image display, statistical modeling, filtering, and despeckling Automatic detection of changes in multitemporal satellite images Bio/geophysical parameter estimation from satellite data and supervised regression methodologies 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 Long D. and Ulaby F. T., 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 GABRIELE MOSER Ricevimento: By appointment Exam Board GABRIELE MOSER (President) MATTEO PASTORINO (President) ANDREA RANDAZZO SEBASTIANO SERPICO LESSONS Class schedule SATELLITE IMAGES EXAMS EXAM DESCRIPTION Oral examination 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 data analysis shall be evaluated.
