Course Title: Conduct advanced remote sensing analysis

Part B: Course Detail

Teaching Period: Term2 2010

Course Code: GEOM5112C

Course Title: Conduct advanced remote sensing analysis

School: 130T Vocational Engineering

Campus: City Campus

Program: C6098 - Advanced Diploma of Spatial Information Services

Course Contact: Program Manager

Course Contact Phone: +61 3 9925 4468

Course Contact Email:

Name and Contact Details of All Other Relevant Staff

Thierry Demathieu

+61 3 9925 8359

William Ntuwah
+61 3 9925 4445

Nominal Hours: 120

Regardless of the mode of delivery, represent a guide to the relative teaching time and student effort required to successfully achieve a particular competency/module. This may include not only scheduled classes or workplace visits but also the amount of effort required to undertake, evaluate and complete all assessment requirements, including any non-classroom activities.

Pre-requisites and Co-requisites


Course Description

This unit of competency specifies the outcomes required to enhance, classify and process remotely sensed data, using both hard copy and digital imagery in a geographic information systems (GIS) context. It requires the ability to apply wide-ranging specialised technical, creative and conceptual skills, a broad knowledge of spatial datasets, and accountability for personal and group outcomes. Functions will entail complying with and developing or amending organisational guidelines.

National Codes, Titles, Elements and Performance Criteria

National Element Code & Title:

CPPSIS6017A Conduct advanced remote sensing analysis


1. Determine appropriate digital image processing techniques.

Performance Criteria:

1.1 Appropriate image, merger and modelling
techniques are determined according to
organisational requirements and project
1.2 Appropriate data collection and analysis techniques
in remote sensing process are determined according
to project requirements.
1.3 Suitable digital image processing techniques and
digital image data formats are selected.
1.4 Additional characteristics of image and metadata
are included.
1.5 OHS issues are considered at all times.
1.6 Skills and knowledge are updated to accommodate
changes in operating environment and equipment.


2. Select suitable computing platforms and software systems.

Performance Criteria:

2.1 Spatial computing platforms and software systems
are assessed for suitability in line with the project
2.2 Availability of suitable data is verified with the
potential suppliers.
2.3 Constraints on use of spatial data are assessed
against specification.
2.4 Commercially available image processing systems
are assessed to determine appropriate components,
menu items, characteristics and statistics.


3. Conduct image enhancements and manipulations.

Performance Criteria:

3.1 Transformation routines using image calculations
are conducted.
3.2 Edge enhancements and smoothing filters are applied
with the use of convolution matrices.
3.3 Image transformation is performed with channels of
brightness, greenness and wetness.
3.4 Imagery for distribution is determined.


4. Perform supervised and unsupervised classifications on datasets.

Performance Criteria:

4.1 Thematic classifications and relative differentiations
between supervised and unsupervised classification
algorithms are determined.
4.2 Supervised classifications of algorithms are
conducted with the use of training areas.
4.3 Hard copy outputs are produced according to
4.4 Error analysis is applied to perform an approximate
accuracy assessment of classifications.


5. Conduct data merger and GIS integration.

Performance Criteria:

5.1 Components of integration and merging techniques
are summarised.
5.2 Techniques of use for the GIS data are documented.

Learning Outcomes

Details of Learning Activities

Teacher led:
Classroom &/or tutorial instruction in Remore sensing and tasks associated with the elements above, mainly to enhance, classify and process remotely sensed data,
using both hard copy and digital imagery in a geographic information systems (GIS) context.

Student managed:

Develop data management strategies to determine suitable sources of information for the creation of new spatial data sets to industry standards.
Participation in group activities based on simulated workplace exercises. Individual exercises carrying out research, computations, data compilation and appraisal.
Compilation of Portfolio of examples of work, client reports, management reports and data management records.

Teaching Schedule

This is an indicative teaching schedule, refer to the Blackboard for any changes or announcements.

Week No.Topic deliveredAssessment/Task
1Introduction to Remote SensingAssignment 20%
2Types of RS systems and EMS 
3Spectral Signature of Objects 
4MultiSpec SOFTWARE – Image analysis 
5Analysis of Remote Sense images - Comparing the Five LandSat ChannelsAssignment 20%
6Supervised and Unsupervised classification 
7Vegetation indices 
8Vegetation indices NDVI Browse Service
Assignment 20%
9Vegetation Indices 
10Change detection 
11Laser scanning 
12Introduction to photogrammetry 
13Relief DisplacementAssignment 20%
15Site visit 
16Site visit 
17Relief DisplacementAssignment 20%
18Site visit 

Learning Resources

Prescribed Texts

Campbell J.B. (1996) Introduction to Remote Sensing, 2nd Edition, The Guildford Press, New York.
Clevers J.G. and Buiten H.J. (1993) Land Information by Remote Sensing: theory and practice, Gordon and Breach Science Publishers, Amsterdam. 

Harrison B.A. and Jupp D.L.B. (1989) Introduction to Remotely Sensed Data, CSIRO Publications, East Melbourne.



Other Resources

Overview of Assessment

Assessment may incorporate a variety of methods including written/oral activities and demonstration of practical skills to the relevant industry standards. Participants are advised that they are likely to be asked to personally demonstrate their assessment activities to their teacher/assessor. Feedback will be provided throughout the course.

Assessment Tasks

Observation, using a skills checklist of performance of elements of competence as designated for this unit to the required standard during practical exercises within a simulated workplace project; including collection methods and techniques related to the project objectives..

Assessment Matrix

Element 1Determine appropriate digital image processing techniques.Assignment  1
Element 2
Select suitable computing platforms and software systems.Assignment  1
Element 3
Conduct image enhancements and manipulations.Assignment  2
Element 4
Perform supervised and unsupervised classifications on datasets.Assignment  2
Element 5
Conduct data merger and GIS integration.Assignment  3

Course Overview: Access Course Overview