AIMS AND CONTENT

LEARNING OUTCOMES

In the framework of natural disaster risk management, the objective of the module is to provide the student with basic knowledge about information extraction from remote sensing images, with special focus on land cover mapping and bio/geophysical parameter retrieval in the context of risk prevention, on change detection in the context of damage assessment, and on the integration of the resulting thematic products in a geographic information system.

AIMS AND LEARNING OUTCOMES

After the module, 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 representation of thematic products in a geographic information system (GIS). In this framework, he/she shall also have methodological bases about machine learning for supervised classification and regression. 

TEACHING METHODS

Class lectures and software laboratory exercizes.

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 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
• GIS tools for data and thematic product representation

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
• 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

LESSONS START

https://corsi.unige.it/en/corsi/10553/studenti-orario

Class schedule

REMOTE SENSING

EXAMS

EXAM DESCRIPTION

Mandatory written examination about the topics in the syllabus of the module, 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 module shall be evaluated. Within the optional oral examination, the student's capability to address simple problems of remote sensing, in applications to natural disaster management, 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.