SPA504 Remote Sensing Algorithms (8)


In this subject, students learn important mathematical concepts and algorithms commonly used in processing of multispectral remote sensed imagery. This includes understanding the sources and characteristics of remote sensed data and the geometric and radiometric corrections to remote sensed imagery. On completion, students will have the ability to select and use an appropriate image processing technique to enhance and classify land cover classes using multispectral image data.

+ Subject Availability Modes and Location

Session 2
DistanceAlbury-Wodonga Campus
Continuing students should consult the SAL for current offering details: SPA504
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

Introductory mathematics at tertiary level and remote sensing background at the level of SPA417 or equivalent.

Enrolment restrictions

Undergraduate students may not enrol in this subject unless they have the permission of their Course Director and the Subject Coordinator. Students who have completed SPA404 may not enrol in this subject
Incompatible subject(s)

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to describe the sources and characteristics of remote sensed data;
  • be able to make the appropriate radiometric and geometric changes to correct remote sensed imagery and to view geometry distortions;
  • be able to implement the specialised mathematical and statistical skills needed to undertake appropriate image processing techniques used in remote sensing;
  • be able to select and analyse the most appropriate image processing techniques to enhance 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 indices, tasseled cap and Taylor transformations
  • Spatial filtering using Fourier transformation techniques
  • Supervised classification 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 canonical variate analysis techniques
  • Analysis of multispectral remote sensed image data - case studies


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.