At the end of this course the student will have to: • know the main theoretical assumptions relateci to remote sensing; • know the main algorithms relateci to the processing of remote sensing data; • know the main procedures relateci to the use of remote sensing in the various application domain. At the end of this course the student will know: • how to describe and correctly interpretate remotely sensed data in digital and cartographic form; • how to autonomously collect and process remotely sensed data; • and will have acquired the tools and adequate skills far the interpretation of data and the extraction of information from remotely sensed data. At the end of this teaching the student will be able to formulate a judgment: • on the quality of remotely sensed data; • on information relating to the main aspects of remote sensing. At the end of the course the student must know: • How to use a correct and adequate language far the communication of information extracted from remote sensing data. At the end of this course the student will have: • the minimum autonomous study skills of remote sensing; • the ability to autonomously investigate the main aspects of remote sensing.

None

• lntroduction to Remote Sensing: definitions and main physical laws • Interaction between atmosphere and electromagnetic radiation: atmospheric windows and scattering • lnteraction between surfaces and electromagnetic radiation: geometrie and physical- chemical charachteristics • Visual perception and colorimetry basics • Satellites far Earth Observation: geostationary and sun-synchronous. Main missions, Sensor scheme, digitai image: numerica! definition and operational features (resolutions) • lmage processing: histogram, contrast enhancement, scatter plots, digitai filters, matrix operators: spectral indices from satellite imagery • lmage interpretation • Radiometric pre-processing: radiance/reflectance calibration, dark subtraction • geometrie preprocessing: image warping • lmage classification: supervised and unsupervised classifiers • Classification Accuracy: confusion matrix and statistica! accuracy parameters

The course includes theoretical lectures (66%) and practical exercises/laboratory sessions (33%)

The teaching is organized in lessons and operational activities developed with the help of the Envi software. The former are dedicated to the presentation of the theoretical and methodological aspects of the phenomena examined; the latter are aimed at understanding, from an operational point of view, the methods of management, processing and extraction of information from large quantities of data acquired by sensors mounted on board of aerial and satellite platforms.

The following material will be available from the beginning of the lesson: • manual of the theoretical part of the entire course • presentation, in PDF format, of each single theoretical lecture • videolessons of all theoretical lectures (both in ltalian and English) • videolessons of ali practical laboratory exercises (both in ltalian and English) • data and materials concerning the carrying out of laboratory exercises

Lecture notes; Video lectures (previous years); Multimedia materials;

Exam: Written test; Optional oral exam; Individual project; Group project;

The final exam assesses the acquisition of the expected knowledge and skills through a 1.5 hour written test. In order to verify the achievement of learning objectives, and thus the acquisition of knowledge, comprehension skills, and the ability to apply them, the test aims to verify the acquisition of the fundamentals of Remote Sensing, focuses on the theory covered during lectures and consists of three open-ended questions, without the use of notes or books. Honors will be awarded if the answers are particularly clear and complete in their presentation.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.