DimensionalityReduction¶
Perform Dimension reduction of the input image.
Description¶
Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image and to optionally export the transformation matrix to a text file.
This application has several output images and supports “multi-writing”. Instead of computing and writing each image independently, the streamed image blocks are written in a synchronous way for each output. The output images will be computed strip by strip, using the available RAM to compute the strip size, and a user defined streaming mode can be specified using the streaming extended filenames (type, mode and value). Note that multi-writing can be disabled using the multi-write extended filename option: &multiwrite=false, in this case the output images will be written one by one. Note that multi-writing is not supported for MPI writers.
Parameters¶
Input Image -in image Mandatory
The input image to apply dimensionality reduction.
Output Image -out image [dtype]
output image. Components are ordered by decreasing eigenvalues.
Rescale Output -rescale [no|minmax] Default value: no
Enable rescaling of the reduced output image.
No rescale
rescale to min max value
rescale to min max value options¶
Output min value -rescale.minmax.outmin float Default value: 0
Minimum value of the output image.
Output max value -rescale.minmax.outmax float Default value: 255
Maximum value of the output image.
** Inverse Output Image** -outinv image [dtype]
reconstruct output image.
Algorithm -method [pca|napca|maf|ica] Default value: pca
Selection of the reduction dimension method.
PCA
Principal Component Analysis.NA-PCA
Noise Adjusted Principal Component Analysis.MAF
Maximum Autocorrelation Factor.ICA
Independent Component Analysis using a stabilized fixed point FastICA algorithm.
PCA options¶
Output file containing eigenvalues (txt format) -method.pca.outeigenvalues filename [dtype]
Output file containing eigenvalues (txt format).
Perform pca whitening -method.pca.whiten bool Default value: true
Perform whitening and ensure uncorrelated outputs with unit component wise variances
NA-PCA options¶
Set the x radius of the sliding window -method.napca.radiusx int Default value: 1
Set the y radius of the sliding window -method.napca.radiusy int Default value: 1
ICA options¶
number of iterations -method.ica.iter int Default value: 20
Give the increment weight of W in [0, 1] -method.ica.mu float Default value: 1
Nonlinearity -method.ica.g [tanh|exp|u3] Default value: tanh
Nonlinearity used in the FastICA algorithm
tanh
g(x) = tanh(x)exp
g(x) = -exp(-x^2/2)u^3
g(x) = u^3(x)
Number of Components -nbcomp int Default value: 0
Number of relevant components kept. By default all components are kept.
Center and reduce data -normalize bool Default value: false
Center and reduce data before Dimensionality reduction (if this parameter is set to false, the data will be centered but not reduced.
Transformation matrix output (text format) -outmatrix filename [dtype]
Filename to store the transformation matrix (csv format)
Background Value -bv float
Background value to ignore in computation of the transformation matrix. Note that all pixels will still be processed when applying the transformation.
Available RAM (MB) -ram int Default value: 256
Available memory for processing (in MB).
Examples¶
From the command-line:
otbcli_DimensionalityReduction -in cupriteSubHsi.tif -out FilterOutput.tif -method pca
From Python:
import otbApplication
app = otbApplication.Registry.CreateApplication("DimensionalityReduction")
app.SetParameterString("in", "cupriteSubHsi.tif")
app.SetParameterString("out", "FilterOutput.tif")
app.SetParameterString("method","pca")
app.ExecuteAndWriteOutput()
Limitations¶
This application does not provide the inverse transform and the transformation matrix export for the MAF. the background value option is not supported for MAF and ICA.