The important thing to remember when gathering evidence is that the more evidence the better - that is, the more evidence you gather to demonstrate your skills, the more confident an assessor can be that you have learned the skills not just at one point in time, but are continuing to apply and develop those skills (as opposed to just learning for the test!). Furthermore, one piece of evidence that you collect will not usualy demonstrate all the required criteria for a unit of competency, whereas multiple overlapping pieces of evidence will usually do the trick!
From the Wiki University
What evidence can you provide to prove your understanding of each of the following citeria?
Determine image processing techniques.
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Project specifications are identified and analysed to determine appropriate image, merger and modelling techniques according to organisational requirements. Completed |
Evidence:
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Appropriate data collection and analysis techniques in remote sensing process are determined according to project specifications. Completed |
Evidence:
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Suitable digital image processing techniques and digital image data formats are selected according to project specifications. Completed |
Evidence:
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Additional characteristics of image and metadata are identified according to project specifications. Completed |
Evidence:
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Select computing platforms and software systems for image processing.
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Appropriate computing platforms and software systems are assessed for suitability according to project specifications. Completed |
Evidence:
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Availability of suitable data is verified with potential suppliers according to project specifications. Completed |
Evidence:
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Constraints on use of spatial data are assessed against project specifications and contingencies are planned according to organisational requirements. Completed |
Evidence:
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Commercially available image processing systems are assessed to determine appropriate components, menu items, characteristics and statistics to meet project specifications. Completed |
Evidence:
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Enhance and manipulate images.
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Transformation routines using image calculations are conducted. Completed |
Evidence:
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Edge enhancements and smoothing filters are applied using convolution matrices. Completed |
Evidence:
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Image transformation is performed with channels of brightness, greenness and wetness. Completed |
Evidence:
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Imagery for distribution is determined according to project specifications. Completed |
Evidence:
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Perform classifications on datasets.
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Thematic classifications and relative differentiations between supervised and unsupervised classification algorithms are determined. Completed |
Evidence:
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Supervised classification algorithms are applied using training samples according to project specifications. Completed |
Evidence:
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Error analysis is conducted to perform an approximate accuracy assessment of classifications. Completed |
Evidence:
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Hard copy outputs are produced according to project specifications. Completed |
Evidence:
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Conduct data merger and GIS integration.
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Integration and merging techniques are identified and documented. Completed |
Evidence:
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Techniques for integrating GIS data are identified and documented. Completed |
Evidence:
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Remote sensing data is merged and integrated into GIS according to project specifications. Completed |
Evidence:
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