Commit 7293ab8e by 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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