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Principal Component Analysis (PCA)
This module calculates a principle component analysis of a stack of images or 3Ds. The PCA yields so-called eigenimages, which facilitate subsequent image classification.
Usage
Example
Process
Parameters | Description |
---|---|
Dimension | Number of calculated dimensions (Eigen images) |
Eigenimages | Use external eigen images |
Using mask | Use an external mask |
Input | Description |
---|---|
Input | Stack of input images |
Eigen Images | External Eigen images |
Mask | External mask |
Output | Description |
---|---|
Eigenimages | Stack of generated Eigen images |
New/Changed Header Values | Description |
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headerValue1 | what does it say? how is it changed? |
headerValue2 | what does it say? how is it changed? |
headerValue3 | what does it say? how is it changed? |
headerValue4 | what does it say? how is it changed? |