DimensionalityReduction¶
对输入图像执行降维。
描述¶
对输入图像执行降维。主成分分析方法、NA-PCA方法、MAF方法、ICA方法都是可用的。还可以计算逆变换以重建图像，并可选地将变换矩阵导出到文本文件。
这个应用程序有几个输出图像，并支持“多写”。不是独立地计算和写入每个图像，而是以同步的方式为每个输出写入流图像块。输出图像将逐条计算，使用可用的RAM计算条带大小，并且可以使用流扩展文件名(类型、模式和值)指定用户定义的流模式。请注意，可以使用多写扩展文件名选项禁用多写，在这种情况下，将逐个写入输出图像。请注意，MPI编写器不支持多重写入。
参数¶
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
重缩放至最小最大值选项¶
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选项¶
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选项¶
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选项¶
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).
实例¶
从命令行执行以下操作：
otbcli_DimensionalityReduction -in cupriteSubHsi.tif -out FilterOutput.tif -method pca
来自Python的评论：
import otbApplication
app = otbApplication.Registry.CreateApplication("DimensionalityReduction")
app.SetParameterString("in", "cupriteSubHsi.tif")
app.SetParameterString("out", "FilterOutput.tif")
app.SetParameterString("method","pca")
app.ExecuteAndWriteOutput()
局限性¶
此应用程序不提供MAF的逆变换和变换矩阵导出。MAF和ICA不支持背景值选项。