Lecturer: Fabio Dell'Acqua  

Course name: Interpretazioni di immagini telerilevate
Course code: 062238
Degree course: Ingegneria per l'Ambiente e il Territorio
Disciplinary field of science: ING-INF/03
University credits: CFU 6
Course website: http://tlclab.unipv.it/sito_tlc/home.do

Specific course objectives

Basic knowledge of what data can be remotely sensed, and what information can be extracted from them in a perspective of analysing the environment. Capability of evaluating the usefulness of different remotely sensed images in solving a given problem. Elementary capability of processing and interpreting remotely sensed images using commercial programs.

Course programme

The Remote Sensing Systems course aims at providing to the Environmental Engineering students some basic knowledge about systems to perform remote sensing in an Earth Observation perspective. The systems are initially addressed, then the produced data is also treated, as well as some techniques for their processing.

Basic concepts
Introduction to the matter.

• What is Remote Sensing.
• Physical principles of remote sensing: interaction between electromagnetic waves and matter. The black body and its emission law.
• Platforms and sensors.
• How a spaceborne platform works.

Sensors
How remote sensing is performed and what it produces.

• Bands of electromagnetic spectrum
• Different types of sensors, classification and characteristics
• Optical sensors: multispectral and hyperspectral.
• Synthetic aperture radar: the EO radar, the concept of synthetic aperture, focussing, operation modes, radar reflectivity
• Remotely sensed data: features and organisation
• Specific examples of sensors, their features and data characteristics

Data processing
Early-stage processing of remotely sensed data.

• Radiometric correction
• Geometric correction
• Enhancemente techniques: histogram-based techniques, filtering.
• Resolution enhancement
• Principal component analysis

Information extraction
Once data has been prepared, how to extract the information of interest.

• Recall of probability theory
• Ground truth
• Classification and classifiers: supervised and unsupervised.
• Training set, test set, ground truth, confusion matrix, k index.
• Class separability.
• Vegetation indexes: types and characteristics.
• Classification of hyperspectral data.
• Information fusion: data-, feature-, decision-level fusion
• Applications.

Practical exercises
Exercises of data processing using ENVI, an ad-hoc programming and processing environment.

Course entry requirements

Knowledge from basic Engineering courses.

Course structure and teaching

Lectures (hours/year in lecture theatre): 40
Practical class (hours/year in lecture theatre): 10
Practicals / Workshops (hours/year in lecture theatre): 0
Project work (hours/year in lecture theatre): 0

Suggested reading materials

Thomas M. Lillesand, Ralph W. Kiefer, Jonathan W. Chipman. Remote Sensing and Image Interpretation - 5th edition. John Wiley & Sons, 2004. ISBN 0-471-15227-7.

John R. Schott. Remote Sensing: The Image Chain Approach. Oxford Univ. Press, 1996. ISBN: 0-1950-8726-7.

Eric C. Barret, Leonard F. Curtis. Introduction to Environmental Remote Sensing. Stanley Thornes Publishers Ltd. ISBN 0-7487-4006-6.

Testing and exams

Final oral examination. Optional, single written test at the end of the course.