B8642 - FONDAMENTI DI TELERILEVAMENTO (1) (LM)

Learning outcomes

At the end of the course, students will acquire the following skills: digital image processing, extraction of thematic information about several land and environmental aspects (geomorphology, urban, cultural heritage, monitoring, etc..) and management of remotely sensed data in remote sensing and GIS softwares.

Course contents

MODULE 1: OPTICAL 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.
• Radiometric and geometric image distortions. Sources of geometric distortions. Introduction to parametric and non-parametric geometric correction models. Introduction to orthorectification (DTM) and georeferencing.
• Main current missions.
  

MODULE 2: RADAR REMOTE SENSING

• Principles of RADAR operation (data acquisition and acquisition geometry), SAR technology (Synthetic Aperture Radar; operating principles, satellite orbits, and data products), microwave and radar frequency bands.
• Interferometric techniques (InSAR and Persistent Scatterer InSAR – PSInSAR), and an introduction to radar polarimetry.

MODULE 3: APPLICATIONS OF OPTICAL REMOTE SENSING (CLASSIFICATION AND SPECTRAL INDICES)

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

Readings/Bibliography

Attending and non attending students

Lecture notes by the teacher (available on Virtuale platform)

Further reading:

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

Basics of Geomatics. Mario A. Gomarasca. Springer.

The Essentials of SAR: A Conceptual View of Synthetic Aperture Radar and Its Remarkable Capabilities. Thomas P. Ager. TomAger LLC (first edition 2021; updated edition 2023)

 

Teaching methods

In-presence lessons, supported by the use of slides, available among the didactic materials. At the beginning of the course, the teacher will provide all the information relating to the program, the texts and the bibliography and the necessary indications for the exam. However, in the last part of the course, practical exercises are foreseen through an open-source software, during which attending students will be able to apply, under the teacher’s supervision, the data processing methodologies learned in the previous theoretical lessons.

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 real cases and to make independent judgments. To access the exam, the student must create a map by applying one or more classification techniques on one digital satellite data (provided by the teacher or chosen by the candidate).The classification must be accompanied by a confusion matrix, overall accuracy, and the Kappa coefficient.

IMPORTANT: Submission of the classification map is only a requirement for eligibility to take the oral examination. The oral examination will assess students' understanding of the topics covered during the lectures and specified in the course syllabus.

The evaluation criteria will contribute to determining the final grade will be:

1) the level of knowledge, deepening and critical understanding of the contents;

2) the ability to argue, establish relationships and logical connections between topics and apply theoretical principles to real cases;

3) the use of appropriate basic technical terminology;

4) the robustness of the reasoning that led to the creation of the classification map.

The final grade will be assigned according to the following evaluation scale:

• 18-21: sufficient performance;
• 22-24: satisfactory performance;
• 25-27: good enough performance;
• 28-29: good performance;
• 30: very good performance;
• 30 cum laude: excellent performance.

 

Exam dates:

November–December 2027: Two exam dates are scheduled and will be arranged in agreement with the students.

January-February 2027: Four exam sessions are scheduled;

June-July 2027: Four exam sessions are scheduled;

September-October 2027: Four exam sessions are scheduled;

In the remaining months, if necessary, additional exam dates can be arranged with the Teacher.

Teaching tools

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

IMPORTANT: Due to the teaching methods adopted and the classroom used for this course (Computer Laboratory), attendance requires that all students complete Modules 1 and 2 of the mandatory Health and Safety training for study activities in advance, through the University's e-learning platform.More information is available at: https://site.unibo.it/tutela-promozione-salute-sicurezza/it/corsi-di-formazione/formazione-obbligatoria-su-sicurezza-e-salute-per-svolgimento-di-tirocinio-tesi-laboratorio

Artificial Intelligence (AI): The course introduces the principles, potential, and limitations of Machine Learning and Artificial Intelligence techniques applied to remote sensing. For the preparation of the classification map, which is a prerequisite for admission to the oral examination, students who choose to develop the workflow using programming languages, as an alternative to the software presented during the course, may use AI tools to support the writing, optimization, or debugging of scripts. Assessment will focus exclusively on the student's ability to understand, justify, and critically discuss the adopted workflow, the implemented methods, and the results obtained.

Students with learning disorders and\or temporary or permanent disabilities: please, contact the office responsible ( https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

Office hours

See the website of Michaela De Giglio