1

Determine appropriate digital image processing techniques.

1.1

Appropriate image, merger and modelling techniques are determined according to organisational requirements and project specifications.

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.

2

Select suitable computing platforms and software systems.

2.1

Spatial computing platforms and software systems are assessed for suitability in line with the project specification.

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.

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.

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 specifications.

4.4

Error analysis is applied to perform an approximate accuracy assessment of classifications.

5

Conduct data merger and GIS integration.

5.1

Components of integration and merging techniques are summarised.

5.2

Techniques of use for the GIS data are documented.

This section describes the essential skills and knowledge and their level, required for this unit.

Required skills

communication skills to:

consult effectively with clients and colleagues

impart knowledge and ideas through graphic, oral, written and visual means

computer skills to develop business documentation

literacy skills to:

assess and use workplace information

conduct web-based searches and use digital techniques

read and write key performance reports, including technical reports

research and evaluate to source SIS educational information

numeracy skills to:

accurately record and collate

analyse errors

conduct image analysis

estimate costs

interpret and analyse statistics

perform mental calculations

undertake complex computations

organisational skills to plan and prioritise activities to meet contractual requirements

project management skills, including ability to meet deadlines

spatial skills to:

exercise precision and accuracy in relation to remote sensing

archive and retrieve spatial data

manage and manipulate spatial data

manage files

solve complex problems relating to height, depth, breadth, dimension, direction and position in actual operational activity and virtual representation

train others in spatial precision techniques

Required knowledge

budgetary mechanisms and restraints

computer platforms and software for image processing systems

data quality integrity

existing spatial datasets and dataset sources

digital image processing techniques

image enhancement, manipulation and merger techniques

information management

metadata

OHS policies

organisational policies and guidelines

relevant legislative, statutory and industry requirements and standards

resource management processes

risk analysis principles

spatial data formats, handling and structure

spatial referencing systems

The evidence guide provides advice on assessment and must be read in conjunction with the performance criteria, required skills and knowledge, the range statement and the Assessment Guidelines for this Training Package.

Overview of assessment

This unit of competency could be assessed on its own or in combination with other units relevant to the job function, for example CPPSIS5059A Determine suitable information sources to create new spatial datasets, and CPPSIS6024A Design a spatial project plan.

Critical aspects for assessment and evidence required to demonstrate competency in this unit

A person who demonstrates competency in this unit must be able to provide evidence of:

applying remote sensing technology

communicating graphically

understanding copyright and ownership constraints

determining data requirements

evaluating suitability of available data against project specifications

identifying sources of data

knowledge of spatial project requirement and spatial referencing systems.

Specific resources for assessment

Resource implications for assessment include access to:

assessment instruments, including personal planner and assessment record book

assignment instructions, work plans and schedules, policy documents and duty statements

registered training provider of assessment services

relevant guidelines, regulations and codes of practice

suitable venue and equipment.

Access must be provided to appropriate learning and assessment support when required.

Where applicable, physical resources should include equipment modified for people with disabilities.

Context of assessment

Holistic: based on the performance criteria, evidence guide, range statement, and required skills and knowledge.

Method of assessment

Demonstrated over a period of time and observed by the assessor (or assessment team working together to conduct the assessment).

Demonstrated competency in a range of situations, that may include customer/workplace interruptions and involvement in related activities normally experienced in the workplace.

Obtained by observing activities in the field and reviewing induction information. If this is not practicable, observation in realistic simulated environments may be substituted.

Guidance information for assessment

Assessment requires that the clients’ objectives and industry expectations are met. If the clients’ objectives are narrowly defined or not representative of industry needs, it may be necessary to refer to portfolio case studies of a variety of SIS requirements to assess competency.

Oral questioning or written assessment and hypothetical situations (scenarios) may be used to assess underpinning knowledge (in assessment situations where the candidate is offered a preference between oral questioning or written assessment, questions are to be identical).

Supplementary evidence may be obtained from relevant authenticated correspondence from existing supervisors, team leaders or specialist training staff.

All practical demonstration must adhere to the safety and environmental regulations relevant to each State or Territory.

Where assessment is for the purpose of recognition (recognition of current competencies [RCC] or recognition of prior learning [RPL]), the evidence provided will need to be authenticated and show that it represents competency demonstrated over a period of time.

In all cases where practical assessment is used it will be combined with targeted questioning to assess the underpinning knowledge.

Assessment processes will be appropriate to the language and literacy levels of the candidate and any cultural issues that may affect responses to the questions, and will reflect the requirements of the competency and the work being performed.

The range statement relates to the unit of competency as a whole. It allows for different work environments and situations that may affect performance. Bold italicised wording in the performance criteria is detailed below. Add any essential operating conditions that may be present with training and assessment depending on the work situation, needs of the candidate, accessibility of the item, and local industry and regional contexts.

Image, merger and modelling techniques may include:

data merging and GIS integration

environmental modelling

supervised and unsupervised classification.

Organisational requirements may include:

Australian Computer Society code of ethics

company policy

legislation relevant to the work or service function

manuals

OHS policy and procedures

personnel practices and guidelines outlining work roles, responsibilities and delegations.

Project specifications refer to:

detailed technical descriptions of the spatial data and its requirements.

Remote sensing process includes:

measurement or acquisition of information of some property of an object or phenomenon by a recording device that is not in physical or intimate contact with object or phenomenon under study, including the practice of data collection in wavelengths from ultraviolet to radio regions

acquisition of information about earth’s surface by electronic and optical instruments from:

satellites

airborne platforms

ground observation.

Digital image processing techniques may include:

image enhancement, including:

contrast

multi-image manipulation

spatial manipulation

image rectification, including:

geometric and digital image correction

noise removal.

Digital image data formats may include:

band interleaved by line (BIL)

band interleaved by pixel (BIP)

band sequential (BSQ)

run length encoding (RLE).

Additional characteristics of image may include:

soil

vegetation bodies

water.

Metadata may include:

summarised information about a spatial dataset that describes the characteristics of the dataset, including:

availability

conditions of use

coordinate system

currency

date of acquisition

quality

source

spatial data acquisition methodologies

version control.

OHS may include:

Australian standards

development of site safety plan

identification of potential hazards

inspection of work sites

training staff in OHS requirements

use of equipment and signage.

Suitabilitymay include:

assessment of whether the spatial data will meet the specification in regard to:

accuracy

completeness

coverage

density

logical consistency.

Availability includes:

assessment of whether the spatial data can be obtained and used for client requirements.

Constraints may include:

administrative

copyright

financial

legal and legislative

technical limitations.

Characteristics and statistics may include:

band selections

hard copy outputs

histogram plots

lookup tables

univariate and multivariate statistics.

Image calculations may include:

greenness ratios

greenness ratios plus dark value

normalised difference vegetation indices.

Error analysis is:

a monitoring process that focuses on scenario planning or possible implications of probable errors.

Integration and merging techniques may include:

change detection

multi-sensor imagery

multi-temporal.

Techniques of use for the GIS data may include:

cartographic modelling

environmental modelling

land cover classification.