LEARNING OUTCOMES

The course introduces the key concepts related to information extraction from remote sensing images in the framework of disaster risk prevention and assessment. Basic knowledge will be provided about remote sensing image acquisition through passive sensors; land cover mapping through remote sensing image classification in the application to risk prevention; detection of ground changes from multitemporal remote sensing images in the application to damage assessment; bio/geophysical parameter estimation through regression methods; and data representation in a geographic information system (GIS).

AIMS AND LEARNING OUTCOMES

After the course, the student shall know the key concepts related to information extraction from remote sensing images in the framework of disaster risk prevention and assessment. He/she shall have basic knowledge about: remote sensing images; land cover mapping through remote sensing image classification in the application to risk prevention; detection of ground changes from multitemporal remote sensing images in the application to damage assessment; bio/geophysical parameter estimation through regression from remote sensing data; and data representation in a geographic information system (GIS).

TEACHING METHODS

Class lectures and laboratory exercizes.

SYLLABUS/CONTENT

• Basic notions and terminology about sensors, platforms, and space orbits for Earth observation
• Remote sensing image acquisition through passive sensors
• Land cover mapping through remote sensing image classification
• Detection of changes through multitemporal remote sensing image analysis
• Bio/geophysical parameter estimation through regression from remote sensing data
• Data representation in a geographic information system

RECOMMENDED READING/BIBLIOGRAPHY

• Richards J. A., Remote sensing digital image analysis, Springer, 2022
• Bishop C., Pattern recognition and machine learning, Springer, 2006
• Campbell J. B. and Wynne R. H., Introduction to remote sensing, Guilford Press, 2011
• 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
• 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.