Lecturer: Fabio Dell'Acqua  

Course name: Interpretation of remotely sensed data
Course code: 064142
Degree course: Ingegneria Elettronica
Disciplinary field of science: ING-INF/03
The course relates to:
University credits: CFU 5
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

Aim of the course is to provide the studens in Electronics Engineering a basic knowledge of Remote Sensing, an ever-increasingly important discipline. The course includes both theoretical/basic aspects and applicative/practical ones (exercises with real-world cases of data processing).

Basic concepts
Introducing the matter

• What is Remote Sesing
• Phisical principles: interaction between electromagnetic waves and matter. The Black Body
• Platforms and sensors.

Sensors
How to perform Remote Sensing, and what comes out of it

• Bands of the electromagnetic spectrum
• Different types of sensors, classification and features
• Optical sensors: multi- and hyper-spectral
• Synthetic aperture radar. The Speckle noise.
• Specific examples of sensors, their features and data characteristics
• The Italian satellite constellation, COSMO/SkyMed

Data processing
How to process remotely sensed data

• Remotely sensed data: features and organisation
• Radiometric correction
• Geometric correction
• Enhancement techniques: histogram-based operations, filtering.
• Resolution enhancement.

Information extraction
Once data have been prepared, how to extract the relevant information.

• Recall on signal theory and stochastic variables
• Classification and thematic maps
• Ground truth
• Supervised vs. unsupervised classification
• Contextual vs. single-item classification
• Training set, test set, confusion matrix, accuracy, k index
• Class separability
• Vegetation index
• Hyperspectral data classification
• Polarimetric radar data
• Data fusion
• Visual interpretation
• Applications

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

Course entry requirements

Knowledge from basic courses of Engineering. It is advisable to attend the "Remote Sensing Systems" course for Electronics Engineering students before attending this course.

Course structure and teaching

Lectures (hours/year in lecture theatre): 34
Practical class (hours/year in lecture theatre): 8
Practicals / Workshops (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.

Testing and exams

Final oral examination.