AIMS AND CONTENT

LEARNING OUTCOMES

Introducing the key concepts related to information extraction from remote-sensing images, in the context of Earth observation for renewable energy applications. Providing basic knowledge about the acquisition and processing of remote sensing images, and the retrieval of bio/geophysical parameters associated with renewable sources, including vegetation biomass, wind velocity over sea water, solar irradiance, and air temperature.

TEACHING METHODS

Class lectures (approximately 38 hours) and software laboratory exercises (approximately 10 hours).

SYLLABUS/CONTENT

1. Introduction to remote sensing for Earth observation: remote sensing and its applications to renewable and traditional energy sources; spaceborne and airborne sensors and platforms; spatial, spectral, temporal, and radiometric resolutions; examples of space missions for Earth observation; digital remote sensing images, their visualization, and contrast enhancement.
2. Remote sensing image acquisition: active radar imaging sensors, side-looking airborne radar and synthetic aperture radar; passive multispectral and hyperspectral sensors; active laser remote sensing (LiDAR); georeferencing and image registration.
3. Biomass mapping through remote sensing: direct and indirect approaches; direct biomass mapping as a regression problem, physically-based methods; indirect biomass mapping from vegetation cover and 3D information; vegetation cover mapping as an image classification problem, basic concepts of statistical pattern recognition, recalling probability theory and random variables, parametric Bayesian and nonparametric classifiers; 3D modelling through LiDAR remote sensing; applications to biomass mapping at various spatial resolutions.
4. Denoising and filtering remote sensing images: noise and speckle in remote sensing images; denoising through linear and rank filters; filters for despeckling; impact on vegetation and biomass mapping.
5. Solar irradiance and air surface temperature mapping through remote sensing: estimation of solar irradiance and irradiation from visible imagery, clear-sky model, cloud indices; air surface temperature regression from thermal infrared imagery, physically-based methods; application to planning and monitoring photovoltaic installations.
6. Mapping the wind velocity field over sea water through remote sensing: wind field, small-scale sea roughness, and their impacts on radar images; scatterometry-based methods for wind speed estimation from radar imagery; image processing methods for wind direction estimation from radar imagery; application to windmill siting.

RECOMMENDED READING/BIBLIOGRAPHY

Richards J. A., Jia X., Remote sensing digital image analysis, 5th ed., Springer-Verlag, 2013
Landgrebe D., Signal theory methods in multispectral remote sensing, John Wiley and Sons., 2003
Oliver C., Quegan S., Understanding synthetic aperture radar images, SciTech Publishing, 2004
Duda R. O., Hart P. E., Stork D. G., Pattern classification, 2nd ed., Wiley, 2001
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)

ANDREA DE GIORGI

SILVANA DELLEPIANE

SEBASTIANO SERPICO

LESSONS

Class schedule

REMOTE SENSING

EXAMS

EXAM DESCRIPTION

Oral examination on the contents of the course; alternately, two partial oral examinations (one around mid-semester and the other at the end of the semester), each focused on approximately half the contents of the course.

Exam schedule