Commit 7293ab8e authored by Jonathan Guinet's avatar Jonathan Guinet

DOC: PCA and NA-PCA doc update.

parent 9334fca0
......@@ -43,6 +43,7 @@
//
// The second Principal Component Analysis is then applied to the
// noise-whitened image, giving the Maximum Noise Fraction transform.
// Applying PCA on noise-whitened image consists in ranking Principal Components according to signal to noise ratio.
//
// It is basically a reformulation of the Maximum Noise Fraction algorithm.
//
......@@ -81,7 +82,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
//
// We start by defining the types for the images and the reader and
// We start by defining the types for the images, the reader and
// the writer. We choose to work with a \doxygen{otb}{VectorImage},
// since we will produce a multi-channel image (the principal
// components) from a multi-channel input image.
......
......@@ -97,7 +97,10 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
//
// The only parameter needed for the PCA is the number of principal
// components required as output. We can choose to get less PCs than
// components required as output. Principal components are linear combination of input components
// (here the input image bands),
// which are selected using Singular Value Decomposition eigen vectors sorted by eigen value.
// We can choose to get less Principal Components than
// the number of input bands.
//
// Software Guide : EndLatex
......@@ -157,7 +160,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
// Figure~\ref{fig:PCA_FILTER} shows the result of applying forward
// and reverse PCA transformation to a 8 bands Wordlview2 image.
// and reverse PCA transformation to a 8 bands Worldview2 image.
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{input-pretty.eps}
......
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