diff --git a/Examples/ChangeDetection/otbMultivariateAlterationDetectorExample.cxx b/Examples/ChangeDetection/otbMultivariateAlterationDetectorExample.cxx
index 3c77125890d87dafd2cf94b9036d77e58c7d070f..acf11d640b008c97b7633ab15d7e7d92cde73823 100644
--- a/Examples/ChangeDetection/otbMultivariateAlterationDetectorExample.cxx
+++ b/Examples/ChangeDetection/otbMultivariateAlterationDetectorExample.cxx
@@ -28,7 +28,7 @@
 //
 //  Software Guide : EndCommandLineArgs
 
-//  Software Guide : BeginLatex 
+//  Software Guide : BeginLatex
 // This example illustrates the class
 // \doxygen{otb}{MultivariateAlterationChangeDetectorImageFilter},
 // which implements the Multivariate Alteration Change Detector
diff --git a/Examples/DimensionReduction/ICAExample.cxx b/Examples/DimensionReduction/ICAExample.cxx
index 5078a9793f1881f4bb8e1fb758e3be56b7adda58..bc3d10ae2f4e38f695aff6dede61b487b01700b8 100644
--- a/Examples/DimensionReduction/ICAExample.cxx
+++ b/Examples/DimensionReduction/ICAExample.cxx
@@ -178,7 +178,7 @@ int main(int argc, char* argv[])
   invFilter->SetMeanValues( FastICAfilter->GetMeanValues() );
   invFilter->SetStdDevValues( FastICAfilter->GetStdDevValues() );
   invFilter->SetTransformationMatrix( FastICAfilter->GetTransformationMatrix() );
-  invFilter->SetPCATransformationMatrix( 
+  invFilter->SetPCATransformationMatrix(
                             FastICAfilter->GetPCATransformationMatrix() );
   invFilter->SetInput(FastICAfilter->GetOutput());
     
diff --git a/Examples/DimensionReduction/MNFExample.cxx b/Examples/DimensionReduction/MNFExample.cxx
index bc4d6df8500e3ab770ef42749d9050fbe48272ed..0d56269f0889c214540066633c622d2aeba28c9c 100644
--- a/Examples/DimensionReduction/MNFExample.cxx
+++ b/Examples/DimensionReduction/MNFExample.cxx
@@ -39,7 +39,7 @@
 // Components Analysis transform. The first transform is based on an
 // estimated covariance matrix of the noise, and intends to whiten the
 // input image (noise with unit variance and no correlation between
-// bands). 
+// bands).
 //
 // The second Principal Components Analysis is then applied to the
 // noise-whitened image, giving the Minimum Noise Fraction transform.
@@ -110,7 +110,7 @@ int main(int argc, char* argv[])
   //
   // Software Guide : EndLatex
 
-  // SoftwareGuide : BeginCodeSnippet  
+  // SoftwareGuide : BeginCodeSnippet
   typedef otb::LocalActivityVectorImageFilter<ImageType,ImageType> NoiseFilterType;
   // SoftwareGuide : EndCodeSnippet
 
diff --git a/Examples/DimensionReduction/MaximumAutocorrelationFactor.cxx b/Examples/DimensionReduction/MaximumAutocorrelationFactor.cxx
index 6dd258860a20ba5981a94e8f6b9a87b37a1f801f..2a96ec70c18fba7f48edfb77c9f8e5bc26d7b584 100644
--- a/Examples/DimensionReduction/MaximumAutocorrelationFactor.cxx
+++ b/Examples/DimensionReduction/MaximumAutocorrelationFactor.cxx
@@ -26,7 +26,7 @@
 //
 //  Software Guide : EndCommandLineArgs
 
-//  Software Guide : BeginLatex 
+//  Software Guide : BeginLatex
 // This example illustrates the class
 // \doxygen{otb}{MaximumAutocorrelationFactorImageFilter}, which
 // performs a Maximum Autocorrelation Factor transform \cite{nielsen2011kernel}. Like
@@ -36,7 +36,7 @@
 //
 // Auto-correlation is the correlation between the component and a
 // unitary shifted version of the component.
-// 
+//
 // Please note that the inverse transform is not implemented yet.
 //
 // We start by including the corresponding header file.
@@ -166,17 +166,17 @@ int main(int argc, char* argv[])
   inputVisuWriter->Update();
   outputVisuWriter->Update();
 
-  //  Software Guide : BeginLatex 
+  //  Software Guide : BeginLatex
   // Figure \ref{fig:MAFFIG} shows the
   // results of Maximum Autocorrelation Factor applied to an 8 bands
-  // Worldview2 image. 
+  // Worldview2 image.
   // \begin{figure}
   // \center \includegraphics[width=0.32\textwidth]{maf-input.eps}
   // \includegraphics[width=0.32\textwidth]{maf-output.eps}
   // \itkcaption[Maximum Autocorrelation Factor results]{Results of the
   // Maximum Autocorrelation Factor algorithm applied to a 8 bands
   // Worldview2 image (3 first components).}  \label{fig:MAFFIG}
-  // \end{figure} 
+  // \end{figure}
   // Software Guide : EndLatex
 
   return EXIT_SUCCESS;
diff --git a/Examples/DimensionReduction/NAPCAExample.cxx b/Examples/DimensionReduction/NAPCAExample.cxx
index 6ecc58b916ab2c1da849393ba0b6217da1f3efa3..448b766e91838aeb2d4316aedbc47876f0897c14 100644
--- a/Examples/DimensionReduction/NAPCAExample.cxx
+++ b/Examples/DimensionReduction/NAPCAExample.cxx
@@ -99,7 +99,7 @@ int main(int argc, char* argv[])
   //
   // Software Guide : EndLatex
 
-  // SoftwareGuide : BeginCodeSnippet  
+  // SoftwareGuide : BeginCodeSnippet
   typedef otb::LocalActivityVectorImageFilter<ImageType,ImageType> NoiseFilterType;
   // SoftwareGuide : EndCodeSnippet
 
diff --git a/Examples/OBIA/RadiometricAttributesLabelMapFilterExample.cxx b/Examples/OBIA/RadiometricAttributesLabelMapFilterExample.cxx
index fb8dc88f59889903ad83c3ba28fdc54f6b75234a..c82eae5c36403acb5ddf2cbeef3df73651ca633e 100644
--- a/Examples/OBIA/RadiometricAttributesLabelMapFilterExample.cxx
+++ b/Examples/OBIA/RadiometricAttributesLabelMapFilterExample.cxx
@@ -86,7 +86,7 @@ int main(int argc, char * argv[])
 
   // Labeled image type
   typedef unsigned short                              LabelType;
-  typedef unsigned char 	                      MaskPixelType;
+  typedef unsigned char                              MaskPixelType;
   typedef double                                      PixelType;
   typedef otb::Image<LabelType, Dimension>            LabeledImageType;
   typedef otb::Image<MaskPixelType, Dimension>        MaskImageType;