SATELLITE IMAGES

SATELLITE IMAGES

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iten
Code
90568
ACADEMIC YEAR
2020/2021
CREDITS
2.5 credits during the 2nd year of 10378 INTERNET AND MULTIMEDIA ENGINEERING (LM-27) GENOVA
SCIENTIFIC DISCIPLINARY SECTOR
ING-INF/03
TEACHING LOCATION
GENOVA (INTERNET AND MULTIMEDIA ENGINEERING)
semester
2° Semester
modules
This unit is a module of:
Teaching materials

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 through machine learning methodologies for supervised regression: regression trees, random forest, support vector regression, neural networks, Gaussian process regression.

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

Ricevimento: By appointment

Exam Board

GABRIELE MOSER (President)

MATTEO PASTORINO (President)

SEBASTIANO SERPICO

ANDREA RANDAZZO

LESSONS

Teaching methods

Class lectures (approximately 20 hours) and laboratory exercizes (approximately 5 hours)

ORARI

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

Vedi anche:

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.