diff --git a/Examples/Radiometry/NDVIRAndNIRVegetationIndexImageFilter.cxx b/Examples/Radiometry/NDVIRAndNIRVegetationIndexImageFilter.cxx index bfe4fca65dea97d1593a8ab4fd0c6e128e6b00f3..74750be8340efad4101b53acfe7b6cd19bffd158 100755 --- a/Examples/Radiometry/NDVIRAndNIRVegetationIndexImageFilter.cxx +++ b/Examples/Radiometry/NDVIRAndNIRVegetationIndexImageFilter.cxx @@ -40,14 +40,14 @@ // \doxygen{otb}{RAndNIRVegetationIndexImageFilter} with the use of the Normalized // Difference Vegatation Index (NDVI). // NDVI computes the difference between the NIR channel, noted $L_{NIR}$, and the red channel, -// noted $L_{r}$ radiances reflected from the surface and transmitted through the atmosphere : +// noted $L_{r}$ radiances reflected from the surface and transmitted through the atmosphere: // // \begin{equation} // \mathbf{NDVI} = \frac{L_{NIR}-L_{r}}{L_{NIR}+L_{r}} // \end{equation} // // With the \doxygen{otb}{RAndNIRVegetationIndexImageFilter} class the filter -// inputs are one channel images : one inmage represents the NIR channel, the +// inputs are one channel images: one inmage represents the NIR channel, the // the other the NIR channel. // // Let's look at the minimal code required to use this algorithm. First, the following header @@ -79,7 +79,7 @@ int main( int argc, char *argv[] ) // Software Guide : BeginLatex // - // The image types are now defined using pixel types and particular + // The image types are now defined using pixel types the // dimension. Input and output images are defined as \doxygen{otb}{Image}. // // Software Guide : EndLatex @@ -101,7 +101,9 @@ int main( int argc, char *argv[] ) // Software Guide : BeginLatex // // The NDVI (Normalized Difference Vegetation Index) is instantiated using - // the images pixel type types as template parameters. + // the images pixel type types as template parameters. It is + // implemented as a functor class which will be passed as a + // parameter to an \doxygen{otb}{RAndNIRVegetationIndexImageFilter}. // // Software Guide : EndLatex @@ -122,7 +124,8 @@ int main( int argc, char *argv[] ) typedef otb::RAndNIRVegetationIndexImageFilter<InputRImageType, InputNIRImageType, OutputImageType, - FunctorType> RAndNIRVegetationIndexImageFilterType; + FunctorType> + RAndNIRVegetationIndexImageFilterType; // Software Guide : EndCodeSnippet @@ -134,7 +137,7 @@ int main( int argc, char *argv[] ) // Software Guide : BeginLatex // - // Now the input images is set and a name is given to the output image. + // Now the input images are set and a name is given to the output image. // // Software Guide : EndLatex @@ -147,8 +150,9 @@ int main( int argc, char *argv[] ) // Software Guide : BeginLatex // - // The filter inputs are linked to readers output and - // the filter output is linked to the writer input. + // We set the processing pipeline: the filter inputs are linked to + // reader output and the filter output is linked to the writer + // input. // // Software Guide : EndLatex @@ -238,15 +242,15 @@ int main( int argc, char *argv[] ) // Software Guide : BeginLatex // // Let's now run this example using as input the images - // \code{NDVI\_3.hdr} and \code{NDVI\_4.hdr} (images kindly and free of charge given by the SISA and the CNES) - // and $\gamma=0.6$ provided in the directory \code{Examples/Data}. + // \code{NDVI\_3.hdr} and \code{NDVI\_4.hdr} (images kindly and free of charge given by SISA and CNES) + // provided in the directory \code{Examples/Data}. // // // \begin{figure} \center // \includegraphics[width=0.24\textwidth]{pretty_Red.eps} // \includegraphics[width=0.24\textwidth]{pretty_NIR.eps} // \includegraphics[width=0.24\textwidth]{pretty_NDVIRAndNIRVegetationIndex.eps} - // \itkcaption[ARVI Example]{NDVI input images on the right (Red channel and NIR channel), on the left the result of the algorithm.} + // \itkcaption[ARVI Example]{NDVI input images on the left (Red channel and NIR channel), on the right the result of the algorithm.} // \label{fig:NDVIRAndNIRVegetationIndex} // \end{figure} //