Corso di Laurea Magistrale Interateneo in Geografia e Scienze Territoriali (LM-80)

Fundamentals of multispectral optical remote sensing aimed at the generation thematic maps in the environmental domain. The focus  is on the free available image datasets having a middle geometric resolution, useful for investigations at the territorial and landscape scale.

KNOWLEDGE AND UNDERSTANDING
At the end of this course the student will have to:
- know the main theoretical assumptions related to remote sensing;
- know the main algorithms related to the processing of remote sensing data;
- know the main procedures related to the use of remote sensing in the various application domains.

APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of this course the student will know:
- describe and correctly interpretate remotely sensed data in numerical and cartographic form.
- autonomously collect and process remotely sensed data.
- have acquired the tools and adequate skills for the interpretation of data and the extraction of information from remotely sensed data.

INDEPENDENT JUDGEMENT
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.

COMMUNICATION SKILLS
At the end of the course the student must know:
- use a correct and adequate language for the communication of information extracted from remote sensing data.

LEARNING SKILLS
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.

Teaching (theory and practice) will be delivered in face-to-face mode directly in class and/or lab. 

The module is made of lectures (40 hours) where theoretical aspects are coupled with practical examples (laboratories, 14 hours) concerning operational processing of satellite data. Students are trained on the basic use of specific processing software (ENVI) useful for the extraction of territorial information from satellite data. Students will be provided with slides and data that the teacher uses during lessons  through the CAMPUSNET system.

Effectiveness of lessons is verified by discussing with the students at the beginning of a new lesson (10 minutes), the content of the last preceding one.  In this context students are invited to answer some technical questions and proposing their own ones.

The exams take place in presence except as provided by the Rectoral Decrees available at the following link:https://www.unito.it/ateneo/gli-speciali/coronavirus-aggiornamenti-la-comunita-universitaria/disposizioni-chi-studia-e

For the 2020/2021 cohort and earlier, the exam will take place face-to-face or electronically with a form to be agreed with the individual teachers. Students are asked to contact the teacher via email.

- manual of the theoretical part of the entire course

- manual of the practical practice of the entire course

- presentation, in PDF format, of each single theoretical lecture

- videolessons of all theoretical lectures (both in Italian and English)

- videolessons of all practical laboratory exercises (both in Italian and English)

- data and materials concerning the carrying out of laboratory exercises

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