Performs dimensionality reduction of the input image based on a dimensionality reduction model file.


This application reduces the dimension of an input image, based on a machine learning model file produced by the TrainDimensionalityReduction application. Pixels of the output image will contain the reduced values fromthe model. The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application.


Input Image -in image Mandatory
The input image to predict.

Input Mask -mask image
The mask allow restricting classification of the input image to the area where mask pixel values are greater than 0.

Model file -model filename [dtype] Mandatory
A dimensionality reduction model file (produced by TrainRegression application).

Statistics file -imstat filename [dtype]
An XML file containing mean and standard deviation to center and reduce samples before prediction (produced by ComputeImagesStatistics application). If this file containsone more bands than the sample size, the last stat of last band will beapplied to expand the output predicted value

Output Image -out image [dtype] Mandatory
Output image containing reduced values

Available RAM (MB) -ram int Default value: 256
Available memory for processing (in MB).


From the command-line:

otbcli_ImageDimensionalityReduction -in QB_1_ortho.tif -imstat EstimateImageStatisticsQB1.xml -model clsvmModelQB1.model -out ReducedImageQB1.tif

From Python:

import otbApplication

app = otbApplication.Registry.CreateApplication("ImageDimensionalityReduction")

app.SetParameterString("in", "QB_1_ortho.tif")
app.SetParameterString("imstat", "EstimateImageStatisticsQB1.xml")
app.SetParameterString("model", "clsvmModelQB1.model")
app.SetParameterString("out", "ReducedImageQB1.tif")



The input image must contain the feature bands used for the model training. If a statistics file was used during training by the Training application, it is mandatory to use the same statistics file for reduction.

See also