No offerings have been identified for this subject in 2016

SPA304 Remote Sensing Algorithms (8)


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 Location

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.

Subject information

Duration Grading System School:
One sessionHD/FLSchool of Environmental Sciences

Assumed Knowledge

MTH102 and STA201

Learning Outcomes

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.


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 2016 CSU Handbook was accurate at the date of publication: 06 September 2016. The University reserves the right to vary the information at any time without notice.