This tool can be used to perform an inverse principal component analysis (PCA) on a set of PCA component images. The component images must have been created using the Principal Component Analysis tool. PCA is a type of data transformation that is used with multi-dimensional data, such as that provided by multi-spectral remotely sensed imagery. PCA is used for numerous applications including data reduction, change detection, and noise reduction. When used as a noise reduction technique, an inverse PCA is performed, leaving out one or more of the high-order PCA components, which account for most of the noise variance in the original data set. The decision of how many components to leave out of the inverse transformation should be based on an analysis of the PCA output, considering the amount of explained variance in each PCA component.
The inverse PCA transformation is based on the eigenvectors and eigenvalues contained in the metadata of the component image header files (.dep). These entries are written into the header files by the Principal Component Analysis tool and must not have been modified for the inverse transformation to work properly.
The following is an example of a Python script using this tool:
wd = pluginHost.getWorkingDirectory()
# You may have multiple input files but they must
# be separated by semicolons in the string.
inputFiles = wd + "input1.dep" + ";" + wd + "input2.dep" + ";" + wd + "input3.dep"
outputSuffix = "invPCA"
args = [inputFiles, outputSuffix]
pluginHost.runPlugin("InversePrincipalComponentAnalysis", args, False)
This is a Groovy script also using this tool:
def wd = pluginHost.getWorkingDirectory()
// You may have multiple input files but they must
// be separated by semicolons in the string.
def inputFiles = wd + "input1.dep" + ";" + wd + "input2.dep" + ";" + wd + "input3.dep"
def outputSuffix = "invPCA"
String[] args = [inputFiles, outputSuffix]
pluginHost.runPlugin("InversePrincipalComponentAnalysis", args, false)