Advanced classification algorithm of raster data

General data

Course ID:	1900-3-ZAK-KT
Erasmus code / ISCED:	07.9 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (unknown)
Course title:	Advanced classification algorithm of raster data
Name in Polish:	Zaawansowane algorytmy klasyfikacji danych rastrowych
Organizational unit:	Faculty of Geography and Regional Studies
Course groups:
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	Polish
Type of course:	obligatory courses
Prerequisites:	Digital Image Processing 1900-3-CPO-KT
Prerequisites (description):	Students should to possess basic knowledge of PC computer and about using Windows operation system. Basics of digital image processing
Mode:	Classroom
Short description:	Teaching of rules of classification using high resolution remote sensing data.
Full description:	Giving knowledge and practical skills of advanced rules of classification and preprocessing remote sensing images to this process. The course is dedicated to familiarize students with the idea of use of hyperspectral remote sensing for environmental studies. Will be presenter aerial and satellite hyperspectral images. Preprocessing (calibration and correction), the creation of local spectral libraries and their application. The student learns the advanced methods of hyperspectral data classification.
Bibliography:	Zagajewski B., Sobczak M., (red.) 2005. Imaging spectroscopy. New quality in environmental studies. EARSeL, Uniwersytet Warszawski WGiSR, Warszawa ERDAS Field Guide, przewodnik geoinformatyczny, 1998. GEOSYSTEMS Polska, Warszawa. Jensen J.R., 1996. Introductory digital image precessing – a remote sensing perspective. 2ed ed. Prentice Hall.
Learning outcomes:	Acquiring of practical and theoretical knowledge about basic rules of digital processing of satellite images.
Assessment methods and assessment criteria:	Evaluation of the course is based on the exercises that the student performs during the classes and the exam (oral and practical).
Practical placement:	-

This course is not currently offered.

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Copyright by University of Warsaw.