SPA404 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

Session 2
Distance	Wagga Wagga

Continuing students should consult the SAL for current offering details: SPA404

Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Assumed Knowledge:

STA409 or MTH101 and STA201 or MTH105 and STA201 or MTH135

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 to select and use appropriate image processing techniques to enhance 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 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 cannonical variate analysis techniques; Analysis of multispectral remote sensed image data - case studies.

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