The course is aimed to supply the theoretical and practical foundations of Remote Sensing. The use of GIS software for the processing of remotely sensed data concerning the sea and the territory is included.
Knowledge and understanding: The student must demonstrate knowing the principles, methods and tools of Remote Sensing needed to solve theoretical and practical problems in the field of surveys by aircraft or satellite.
Ability to Apply Knowledge and Understanding: The student must demonstrate the ability to use theoretical and practical concepts acquired for remote surveys as well as for processing data acquired by satellite or aircraft.
Judging autonomy: Students must be able to independently evaluate situations that are different from the standard ones presented by the teacher during the course and to adopt the best methodologies.
Communicative Skills: The student must have the ability to develop and present themes concerning Remote Sensing by using the technical-scientific language correctly.
Learning Skills: The student must be able to update continuously his knowledge through consultation of texts and publications in order to acquire the ability to deepen the topics of Remote Sensing.
Basic knowledge of Physics and Mathematics is required.
Physical principles of Remote Sensing - Radiation and electromagnetic spectrum. Interaction of electromagnetic waves with matter: laws of Kirchhoff, Planck, Stefan-Boltzmann, Wien. Interferences of electromagnetic waves with the atmosphere. Spectral Signatures: Geological and Biological Materials.
The Remote Sensing Platforms - Artificial Satellites: Geostationary satellites and Sun-synchronous satellites. Principal Space Missions for Earth Observation at medium and high ground resolution: Landsat; SPOT; IKONOS; Quickbird; GeoEye; WorldView.
Sensors for remote sensing - Active sensors and passive sensors. Optical Image Sensors: Photo sensors and scanning sensors. Measuring sensors: laser. Microwave sensors: the image radar.
Processing of remotely sensed data - Digital data formats. The statistics applied to the images. Corrections of remotely sensed data: internal errors and external errors; radiometric corrections; geometric corrections by simple and rational polynomial functions. Improving images: using filters.
Remote Sensing Classification Techniques - Vegetation Indices: Ratio, NDVI, Tasseled Cap. Unsupervised Classification: K-means method. Supervised classification: monochrome and multispectral image classifiers; minimum distance classifier; maximum likelihood classifier.
Applications and Tutorials: Using free and open source GIS software to process remotely sensed images; applications on the supervised and unsupervised classification; construction of thematic maps concerning the sea and the territory starting from remotely sensed data.
The course includes the following topics.
Physical principles of Remote Sensing.
The Remote Sensing Platforms.
Sensors for remote sensing.
Processing of remotely sensed data.
Remote Sensing Classification Techniques.
Applications and Tutorials.
The Course includes lectures as well as exercises in computer lab, using free and open source software for processing remotely sensed images.
The didactic material is provided in pdf format by the teacher.
Brivio, P. A., Zilioli, E., & Lechi, G.L. (2006). Principles and methods of remote sensing. CittàStudi.
Joseph, G. Fundamentals of Remote Sensing. Universities Press. 2005
Examination consists in verifying the level of achievement of the above mentioned training objectives.
Exam is just oral and includes discussions of the exercises carried out during the course and regarding the use of free and open source GIS software to process remotely sensed images.