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#### 4.5.1 Dimensionality reduction application

Perform Dimension reduction of the input image.

##### Detailed description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available.

##### Parameters

This section describes in details the parameters available for this application. Table 4.27, page 387 presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is DimensionalityReduction.

Parameter key | Parameter type | Parameter description
in | Input image | Input Image
out | Output image | Output Image
rescale | Group | Rescale Output.
rescale.outmin | Float | Output min value
rescale.outmax | Float | Output max value
outinv | Output image | Inverse Output Image
method | Choices | Algorithm
method pca | Choice | PCA
method napca | Choice | NA-PCA
method maf | Choice | MAF
method ica | Choice | ICA
method.napca.radiusx | Int | Set the x radius of the sliding window.
method.napca.radiusy | Int | Set the y radius of the sliding window.
method.ica.iter | Int | number of iterations
method.ica.mu | Float | Give the increment weight of W in [0, 1]
nbcomp | Int | Number of Components.
normalize | Boolean | Normalize.

Table 4.27: Parameters table for Dimensionality reduction application.

Input Image The input image to apply dimensionality reduction.

Output Image output image. Components are ordered by decreasing eigenvalues.

Rescale Output.

- Output min value: Minimum value of the output image.
- Output max value: Maximum value of the output image.

Inverse Output Image reconstruct output image.

Algorithm Selection of the reduction dimension method. Available choices are:

- PCA: Principal Component Analysis.
- NA-PCA: Noise Adjusted Principal Component Analysis.
  - Set the x radius of the sliding window.:
  - Set the y radius of the sliding window.:
- MAF: Maximum Autocorrelation Factor.
- ICA: Independant Component Analysis.
  - number of iterations :
  - Give the increment weight of W in [0, 1]:

Number of Components. Number of relevant components kept. By default all components are kept.

Normalize. center AND reduce data before Dimensionality reduction.

##### Example

To run this example in command-line, use the following:

otbcli_DimensionalityReduction -in cupriteSubHsi.tif -out FilterOutput.tif -method pca

To run this example from Python, use the following code snippet:

#!/usr/bin/python
# Import the otb applications package
import otbApplication
# The following line creates an instance of the DimensionalityReduction application
DimensionalityReduction = otbApplication.Registry.CreateApplication("DimensionalityReduction")
# The following lines set all the application parameters:
DimensionalityReduction.SetParameterString("in", "cupriteSubHsi.tif")
DimensionalityReduction.SetParameterString("out", "FilterOutput.tif")
DimensionalityReduction.SetParameterString("method","pca")
# The following line execute the application
DimensionalityReduction.ExecuteAndWriteOutput()

##### Limitations

Though the inverse transform can be computed, this application only provides the forward transform for now.

##### Authors

This application has been written by OTB-Team.

##### See also

These additional ressources can be useful for further information:

- ”Kernel maximum autocorrelation factor and minimum noise fraction transformations,” IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

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