SPA304 Remote Sensing Algorithms (8)
CSU Discipline Area: Spatial Science (SPASC)
Duration: One session
Abstract:
This subject aims to give students an understanding of the important mathematical concepts and algorithms commonly used in processing, multispectral, remote sensed, imagery.
+ Subject Availability Modes and Locations
No offerings have been identified for this subject in 2013.Continuing students should consult the SAL for current offering details prior to contacting their course coordinator: SPA304
Where differences exist between the handbook and the SAL, the SAL should be taken as containing the correct subject offering details.
Assumed Knowledge:
Objectives:
Upon successful completion of this subject, students should:
Have an understanding of the sources and characteristics of remote sensed data;
Have an awareness of the radiometric and geometric corrections that are required to correct remote sensed imagery for atmospheric and view geometry distortions;
Have an understanding of the mathematical and statistical formulation of image processing techniques commonly used in remote sensing;
Be able select and use appropriate image processing techniques to ehance and classify land cover classes using multispectral image data.
Syllabus:
The subject will cover the following topics:
Sources and characteristics of remote sensed data; Geometric and radiometric correction imagery; Radiometric enhancement techniques including look up tables, linear contrast enhancement, histogram equalisation, histogram matching, density slicing and pseudocolour techniques; Geometric enhancement techniques including smoothing, edge detection and enhancement, spatial derivatives and general convolution filtering; Multispectral transformations of image data including principal component analysis, band ratios, vegetation indicies, tasseled cap and Taylor transformations; Spatial filtering using Fourier transformation techniques; Supervised clasification of multispectral imagery including maximum likelihood, box, Euclidean distance and Mahalanobis classifiers; Advanced classification techniques including contextural, neural network and expert system classifiers; Unsupervised classification techniques; Feature reduction and land cover discrimination using cannonical variate analysis techniques; Analysis of multispectral remote sensed image data - case studies.
Residential School
This subject contains a optional 2 day residential school.
The information contained in the 2013 CSU Handbook was accurate at the date of publication: 24 April 2013. The University reserves the right to vary the information at any time without notice.