DOC: PCA and NA-PCA doc update.

Examples/DimensionReduction/NAPCAExample.cxx

@@ -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.

Examples/DimensionReduction/PCAExample.cxx

@@ -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}