70077 - Remote sensing (1) (LM)

Learning outcomes

At the end of the course students, who already possess knowledge of the basic principles of cartographic representation, digital mapping and geographic information systems (GIS), will acquire the following skills: digital image processing, the extraction of thematic information about several land and environmental aspects (geomorphology, urban, connected to cultural heritage, monitoring, etc..) and the management of remotely sensed data in GIS software.

Course contents

The course will be held by Dr. Michaela De Giglio (For information: michaela.degiglio@unibo.it) using the logistic support of the Informatic Laboratory, Geography DiSCi Department, Via Guerrazzi 20.

 

MODULE 1: BASIC PRINCIPLES OF REMOTE SENSING

Remote Sensing introduction: definition, general system aspects, applications. Physical principles (from radiant energy to radiance, electromagnetic wave, wavelength, electromagnetic spectrum). Energy interactions with the atmosphere (reflection, transmission and emission). Atmospheric windows. Spectral signature: definition and description of water, soils and rocks, vegetation signatures. Observation tools: platforms, orbits, active and passive sensors, acquisition parameters (IFOV, Swath, satellite elevation angle, sun elevation angle), cross-track, cross-track systems, matrix systems. Acquisition Mode. Description of sensors, digital images, multispectral shooting, data display, geometric resolution, spectral resolution (pan-

sharpening), radiometric resolution, time resolution.

MODULE 2: DISTURBANCES AND CORRECTION

Radiometric and geometric image distortions. Geometric distortion sources (earth bending, rotation, altitude variation, speed, trim, panoramic distortion). Parametric and non-parametric geometric correction models (notes). Orthorectification (DTM), georeferencing. Main current missions.

MODULE 3: CLASSIFICATION

Gaussian distribution. Classification theory. Pixel classification, supervised classification (preliminary steps, classification algorithms - minimum distance from the average). Main supervised algorithms (parallelepiped, maximum likelihood, angular distance); Hard and soft algorithms (Bayes, Fuzzy). Unsupervised classification techniques (cluster, k-means, isodata). Pixel-oriented classification. Accuracy evaluation. PCA, image enhancement techniques, vegetation index.

Readings/Bibliography

Attending and non attending students

Lecture notes by the teacher.

Further reading:

Principi e metodi di telerilevamento. Pietro Alessandro Brivio, Giovanni Lechi, Eugenio Zilioli. Città studi edizioni.

Basics of Geomatics. Mario A. Gomarasca. Springer.

 

Teaching methods

Lectures, practical exercises through the use of software

Assessment methods

Attending and non attending students

Oral test. During the exam, the professor will assess the candidate's comprehension and cross knowledge of the topics covered by the program. Also, the student will be required to apply the remote sensing fundamental concepts to concrete cases and to make independent judgements. Finally, the Professor will verify the acquisition of basic technical terms that characterize this science.

Teaching tools

Educational laboratory of Geomatics/Informatic with individual PC workstations, Open Source and trade software for digital image processing; Open Source GIS software; projector

Office hours

See the website of Michaela De Giglio