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

Specific objectives include providing the students with knowledge 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 and pattern recognition disciplines. Nevertheless, the focus will be on specific topics of prominent interest in the Earth observation field rather than on general purpose data processing and recognition.

TEACHING METHODS

Combination of class lectures and laboratory exercises

SYLLABUS/CONTENT

• Space missions for Earth observation and their applications
• Methods for satellite image display, statistical modeling, filtering (especially despeckling)
• Automatic detection of changes in multitemporal satellite images
• Bio/geophysical parameter estimation from satellite data

RECOMMENDED READING/BIBLIOGRAPHY

​Richards J. A., Remote sensing digital image analysis, Springer, 2013
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
Campbell J. B. and Wynne R. H., Introduction to remote sensing, Guilford Press, 2011
Li S. Z., Markov random field modeling in image analysis, Springer, 2009
Vapnik V., The nature of statistical learning theory, Springer, 2000
Goodfellow I., Bengio Y., and Courville A., Deep learning, MIT Press, 2016
Moser G., Analisi di immagini telerilevate per osservazione della Terra, ECIG, 2007
Class slides will be provided to the students through AulaWeb.

TEACHERS AND EXAM BOARD

Exam Board

GABRIELE MOSER (President)

MATTEO PASTORINO (President)

ANDREA RANDAZZO

SEBASTIANO SERPICO

LESSONS

Class schedule

SATELLITE IMAGES

EXAMS

EXAM DESCRIPTION

Oral examination

ASSESSMENT METHODS

Oral examination on the course topics

Exam schedule