Course Syllabus
Sustainable Development Goals
Aims
The course objective is to teach students the basics of instruments and signal processing techniques with application on Earth Observation and Environmental monitoring. The course aims to enable the students to understand: (i) the nature of remote/proximal sensing signal and how they are acquired; (ii) different types of instruments and measurement techniques (iii) basic signal representation and processing (iv) how to retrieve information from remote/proximal sensing data. Emerging AI based approaches are discussed together with state-of-art semi-empirical and physical-based model inversion methods.
Contents
The course covers fundamental concepts about the acquisition, interpretation, and processing methods of different type of signals ranging from multispectral and hyperspectral spectroradiometer, to seismic, acoustic, and other electromagnetic data. The course also includes applied remote sensing topics aimed to characterize Earth surfaces and Environmental variables and processes.
Detailed program
REMOTE SENSING FUNDAMENTALS
● Physical principles for Earth Remote Sensing
● Remote sensing systems and resolutions
● Multispectral/Hyperspectral spectroradiometers
● Multi-scale sensing (satellite, drone, ground-based)
SIGNAL PROCESSING METHODS FOR EARTH REMOTE SENSING
● Spectral signature of Earth surfaces in the optical domain
● Radiometric/spectral/atmospheric processing
● Examples of Radiative Transfer model simulations
● Spectral indices and spectral transformations
● Retrieval of Earth surface geophysical variables
● Time-series analysis
SIGNAL PROCESSING METHODS FOR PROXIMAL SENSING OF ENVIRONMENT
● Sensors for Environmental monitoring
○ Pressure and thermal sensors
○ Vibration and electromagnetic sensors: seismometers, accelerometers, microphones, antennas
● Acoustic and seismic digital signal processing: time and frequency domain
● Analysis of the sensors data and correlation with geophysical variables
● Noise decorrelation utilizing multiple sensors
● Application of AI for the data analysis and perspectives
Prerequisites
Basic knowledge on physics, computer programming, mathematical and statistical analysis, usually acquired from Bachelor-level courses.
Teaching form
The course is structured in classroom lectures and a computing laboratory. Although not strictly required, attendance to the lectures and practical sessions is strongly recommended. Lectures will be generally held in presence, unless further COVID-19 related restrictions are imposed.
Textbook and teaching resource
● Shunlin Liang, Xiaowen Li and Jindi Wang (2012) Advanced Remote Sensing: Terrestrial Information Extraction and Applications. [S.l.]: Academic Press.
● Slides, scientific manuscripts and handouts are available on the course website.
Semester
First
Assessment method
Practical and oral exam. The student develops a practical project based on the course topics on an environmental application. The oral examination consists in a discussion of the project and an assessment of the theoretical foundations knowledge.
Office hours
Via appointment by email.
