 2014 - 2015 | |||||||||||||||||||||||||||||
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| 0691-4434-01 | Remote Sensing and Photo Processing | ||||||||||||||||||||||||||||
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| FACULTY OF HUMANITIES | |||||||||||||||||||||||||||||
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University credit hours: 4.0
Course description
This is a continuation of the course “Introduction to Remote Sensing” (or its equivalent). It aims at expanding the student's knowledge about remote sensing in general and optical remote sensing in particular. The course will have both theoretical and practical parts and provides the basis for an advanced course in remote sensing such as hyperspectral remote sensing (see other syllabi).
The main topics: electromagnetic radiation, the Plank equation and its derivatives, atmospheric windows and shelters, sensor optics, sensor platforms, spatial and spectral arrangement of RS data, the eyes and the brain as analogues for optical remote sensing, preparation of raw data for analysis, radiometric and atmospheric calibrations, QA/QI of RS data, classification based on color composition and its statistical basis, spatial and spectral filtering, object enhancement, spectral finger prints, spectral-based classification, validation analysis, supervised and unsupervised classification, geometric correction, space programs, satellites and sensors, the economic impact of RS techniques.
The course will be followed by an active tutorial using ENVI software.
Prerequisite:
Introduction to Remote Sensing (or an equivalent)
Grade:
Exam: 70%
Exercises: 20%
Final project: 10%.
The main topics: electromagnetic radiation, the Plank equation and its derivatives, atmospheric windows and shelters, sensor optics, sensor platforms, spatial and spectral arrangement of RS data, the eyes and the brain as analogues for optical remote sensing, preparation of raw data for analysis, radiometric and atmospheric calibrations, QA/QI of RS data, classification based on color composition and its statistical basis, spatial and spectral filtering, object enhancement, spectral finger prints, spectral-based classification, validation analysis, supervised and unsupervised classification, geometric correction, space programs, satellites and sensors, the economic impact of RS techniques.
The course will be followed by an active tutorial using ENVI software.
Prerequisite:
Introduction to Remote Sensing (or an equivalent)
Grade:
Exam: 70%
Exercises: 20%
Final project: 10%.