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Last Post 16 Jul 2017 02:21 PM by  Edward Kelly
Applying SAM, SVM and MLC to PCA
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Edward Kelly



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11 Jul 2017 12:06 PM
    Hi, I intend to apply the Spectral Angle Mapper (SAM), Maximum Likelihood (MLC) and Support Vector Machine (SVM) algorithms on a hyperspectral dataset but would just like to clarify the steps required. I have performed radiometric correction and a forward PCA rotation which resulted in 3 PCA bands. However, I am slightly confused regarding endmember selection procedures when using PCA.

    For example, for the MLC algorithm I understand that if I wasnt using PCA I would simply create new ROIs to derive endmembers. However, when using PCA as a dimensionality reduction technique, I am not sure where to derive the endmembers from. Is it from the original radiance image or from the PCA components?

    Thanks.

    Mari Minari



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    12 Jul 2017 02:14 PM
    If you are going to use data dimensionality reduction, I would suggest you use the Spectral Hourglass wizard which walks you through the process with explanations:
    https://www.harrisgeospatial.com/do...izard.html

    You can get endmembers from the original data, PCA data or from pre-existing spectral libraries taken in the lab. What is key is that the endmembers are 'good' examples/representations of the materials of interest.

    Edward Kelly



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    13 Jul 2017 01:54 PM
    Thanks, yes I have collected endmembers through ROIs which I will export to a spectral library.

    Edward Kelly



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    13 Jul 2017 03:37 PM
    Is there a way to use PCA with Maximum Likelihood, as the Spectral Hourglass Wizard doesn't have an option for it?

    Thanks

    Edward Kelly



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    16 Jul 2017 01:54 PM
    Is it acceptable to use the output from the Forward PCA transform as the input for MLC in the Classification Workflow? Although my training data (which i derived from my original radiance image) don't seem to load from the .xml file when i try to use them in the Classification Workflow.

    Thanks

    Edward Kelly



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    16 Jul 2017 02:21 PM
    Sorry, I may have misunderstood, I think I need to use Inverse PCA rotation to recreate the original image data using the 3 PCA images, and then use this as input into the classification techniques.
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