diff --git a/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.h b/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.h
index 7f44c076bf44d10cf10006cfd18832f64f0084d9..bf42c9489507eadc5c68a5a03ccd3d8415e63f17 100644
--- a/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.h
+++ b/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.h
@@ -33,28 +33,28 @@ namespace otb
 
 /** \class MaximumAutocorrelationFactorImageFilter
  * \brief This filter implements the Maximum Autocorrelation Factor
- * 
+ *
  * This filter implements the Maximum Autocorrelation Factor, based
  * on the following work:
- * 
+ *
  * A. A. Nielsen, "Kernel maximum autocorrelation factor and minimum
  * noise fraction transformations," IEEE Transactions on Image
  * Processing, vol. 20, no. 3, pp. 612-624, (2011)
- * 
+ *
  * Maximum Autocorrelation Factor can be considered as a spatial
  * extension of the PCA, in which new variates try to maximize
  * auto-correlation between neighboring pixels instead of
  * variance. Though the inverse transform can be computed, this filter
  * only provides the forward transform for now.
- * 
+ *
  * The GetV() method allows to retrieve the linear combinations used
  * to generate new variates, and the GetAutoCorrelation() method
  * allows to retrieve the auto-correlation associated to each variate.
- * 
+ *
  * This filter has been implemented from the Matlab code kindly made
  * available by the authors here:
  * http://www2.imm.dtu.dk/~aa/software.html
- * 
+ *
  * this filter have been validated by comparing the output image to
  * the output produced by the Matlab code, and the reference image
  * for testing has been generated from the Matlab code using Octave.
@@ -63,8 +63,8 @@ namespace otb
  * \sa otbPCAImageFilter
  */
 template <class TInputImage, class TOutputImage>
-class ITK_EXPORT MaximumAutocorrelationFactorImageFilter 
-  : public itk::ImageToImageFilter<TInputImage,TOutputImage>
+class ITK_EXPORT MaximumAutocorrelationFactorImageFilter
+  : public itk::ImageToImageFilter<TInputImage, TOutputImage>
 {
 public:
   /** Standard class typedefs. */
@@ -93,10 +93,10 @@ public:
   typedef typename OutputImageType::RegionType                    OutputImageRegionType;
   typedef typename OutputImageType::PixelType                     OutputImagePixelType;
   typedef typename InputImageType::InternalPixelType              InputInternalPixelType;
-  typedef typename 
+  typedef typename
     itk::NumericTraits<InputInternalPixelType>::RealType          InternalPixelType;
 
-  typedef VectorImage<InternalPixelType,2>                        InternalImageType;
+  typedef VectorImage<InternalPixelType, 2>                        InternalImageType;
 
 
   /** Internal filters types */
@@ -112,28 +112,28 @@ public:
   typedef vnl_matrix<RealType>                                    VnlMatrixType;
 
   /** Get the linear correlation used to compute Maf */
-  itkGetMacro(V,VnlMatrixType);
+  itkGetMacro(V, VnlMatrixType);
 
   /** Get the auto-correlation associated with each Maf */
-  itkGetMacro(AutoCorrelation,VnlVectorType);
+  itkGetMacro(AutoCorrelation, VnlVectorType);
 
   /** Get the covariance estimator for image (use for progress
    * reporting purposes) */
-  itkGetObjectMacro(CovarianceEstimator,CovarianceEstimatorType);
+  itkGetObjectMacro(CovarianceEstimator, CovarianceEstimatorType);
 
   /** Get the covariance estimator for horizontal autocorrelation (use
    * for progress reporting purposes) */
-  itkGetObjectMacro(CovarianceEstimatorH,CovarianceEstimatorType);
+  itkGetObjectMacro(CovarianceEstimatorH, CovarianceEstimatorType);
 
   /** Get the covariance estimator for vertical autocorrelation (use
    * for progress reporting purposes) */
-  itkGetObjectMacro(CovarianceEstimatorV,CovarianceEstimatorType);
+  itkGetObjectMacro(CovarianceEstimatorV, CovarianceEstimatorType);
 
 protected:
   MaximumAutocorrelationFactorImageFilter();
   virtual ~MaximumAutocorrelationFactorImageFilter() {}
 
-  virtual void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread,int threadId);
+  virtual void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId);
 
   virtual void GenerateOutputInformation();
 
diff --git a/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.txx b/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.txx
index 1dab724077bbee6a23939cc04f30beccb39145b5..fb7b068c769f64e3f9fff82c9377476595695c93 100644
--- a/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.txx
+++ b/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.txx
@@ -32,7 +32,7 @@
 namespace otb
 {
 template <class TInputImage, class TOutputImage>
-MaximumAutocorrelationFactorImageFilter<TInputImage,TOutputImage>
+MaximumAutocorrelationFactorImageFilter<TInputImage, TOutputImage>
 ::MaximumAutocorrelationFactorImageFilter()
 {
   m_CovarianceEstimator = CovarianceEstimatorType::New();
@@ -42,7 +42,7 @@ MaximumAutocorrelationFactorImageFilter<TInputImage,TOutputImage>
 
 template <class TInputImage, class TOutputImage>
 void
-MaximumAutocorrelationFactorImageFilter<TInputImage,TOutputImage>
+MaximumAutocorrelationFactorImageFilter<TInputImage, TOutputImage>
 ::GenerateOutputInformation()
 {
   // Call superclass implementation
@@ -56,8 +56,8 @@ MaximumAutocorrelationFactorImageFilter<TInputImage,TOutputImage>
   unsigned int nbComp = inputPtr->GetNumberOfComponentsPerPixel();
 
   // Compute Dh and Dv
-  typedef otb::MultiChannelExtractROI<typename InputImageType::InternalPixelType,RealType> ExtractFilterType;
-  typedef itk::SubtractImageFilter<InternalImageType,InternalImageType,InternalImageType>  DifferenceFilterType;
+  typedef otb::MultiChannelExtractROI<typename InputImageType::InternalPixelType, RealType> ExtractFilterType;
+  typedef itk::SubtractImageFilter<InternalImageType, InternalImageType, InternalImageType>  DifferenceFilterType;
 
   InputImageRegionType largestInputRegion = inputPtr->GetLargestPossibleRegion();
   InputImageRegionType referenceRegion;
@@ -120,37 +120,37 @@ MaximumAutocorrelationFactorImageFilter<TInputImage,TOutputImage>
   m_CovarianceEstimator->Update();
   VnlMatrixType sigma = m_CovarianceEstimator->GetCovariance().GetVnlMatrix();
 
-  m_Mean = VnlVectorType(nbComp,0);
+  m_Mean = VnlVectorType(nbComp, 0);
 
-  for(unsigned int i = 0; i<nbComp;++i)
+  for(unsigned int i = 0; i<nbComp; ++i)
     {
     m_Mean[i] = m_CovarianceEstimator->GetMean()[i];
     }
 
-  vnl_generalized_eigensystem ges(sigmad,sigma);
+  vnl_generalized_eigensystem ges(sigmad, sigma);
   VnlMatrixType d = ges.D;
   m_V = ges.V;
 
-  m_AutoCorrelation = VnlVectorType(nbComp,1.);
+  m_AutoCorrelation = VnlVectorType(nbComp, 1.);
   m_AutoCorrelation -= 0.5 *d.get_diagonal();
 
-  VnlMatrixType invstderr = VnlMatrixType(nbComp,nbComp,0);
+  VnlMatrixType invstderr = VnlMatrixType(nbComp, nbComp, 0);
   invstderr.set_diagonal(sigma.get_diagonal());
   invstderr = invstderr.apply(&vcl_sqrt);
   invstderr = invstderr.apply(&InverseValue);
 
-  VnlMatrixType invstderrmaf = VnlMatrixType(nbComp,nbComp,0);
+  VnlMatrixType invstderrmaf = VnlMatrixType(nbComp, nbComp, 0);
   invstderrmaf.set_diagonal((m_V.transpose() * sigma * m_V).get_diagonal());
   invstderrmaf = invstderrmaf.apply(&vcl_sqrt);
   invstderrmaf = invstderrmaf.apply(&InverseValue);
 
   VnlMatrixType aux1 = invstderr * sigma * m_V * invstderrmaf;
 
-  VnlMatrixType sign = VnlMatrixType(nbComp,nbComp,0);
+  VnlMatrixType sign = VnlMatrixType(nbComp, nbComp, 0);
 
-  VnlVectorType aux2 = VnlVectorType(nbComp,0);
+  VnlVectorType aux2 = VnlVectorType(nbComp, 0);
   
-  for(unsigned int i = 0; i < nbComp;++i)
+  for(unsigned int i = 0; i < nbComp; ++i)
     {
     aux2=aux2 + aux1.get_row(i);
     }
@@ -165,7 +165,7 @@ MaximumAutocorrelationFactorImageFilter<TInputImage,TOutputImage>
 
 template <class TInputImage, class TOutputImage>
 void
-MaximumAutocorrelationFactorImageFilter<TInputImage,TOutputImage>
+MaximumAutocorrelationFactorImageFilter<TInputImage, TOutputImage>
 ::ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId)
 {
   // Retrieve input images pointers
@@ -176,7 +176,7 @@ MaximumAutocorrelationFactorImageFilter<TInputImage,TOutputImage>
   typedef itk::ImageRegionConstIterator<InputImageType>  ConstIteratorType;
   typedef itk::ImageRegionIterator<OutputImageType> IteratorType;
 
-  IteratorType outIt(outputPtr,outputRegionForThread);
+  IteratorType outIt(outputPtr, outputRegionForThread);
   ConstIteratorType inIt(inputPtr, outputRegionForThread);
 
   inIt.GoToBegin();
@@ -190,10 +190,10 @@ MaximumAutocorrelationFactorImageFilter<TInputImage,TOutputImage>
 
   while(!inIt.IsAtEnd() && !outIt.IsAtEnd())
     {
-    VnlVectorType x(outNbComp,0);
-    VnlVectorType maf(outNbComp,0);
+    VnlVectorType x(outNbComp, 0);
+    VnlVectorType maf(outNbComp, 0);
     
-    for(unsigned int i = 0; i < outNbComp;++i)
+    for(unsigned int i = 0; i < outNbComp; ++i)
       {
       x[i] = inIt.Get()[i];
       }
diff --git a/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.h b/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.h
index 0454de56b3a5ad0c69a0badcd85d057e15cdd81d..e3db436fd5f22dd7860bdf8415d27d2b94b821e0 100644
--- a/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.h
+++ b/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.h
@@ -31,41 +31,41 @@ namespace otb
 
 /** \class MultivariateAlterationDetectorImageFilter
  * \brief This filter implements the Multivariate Alteration Detector
- * 
+ *
  * This filter implements the Multivariate Alteration Detector, based
  * on the following work:
- * 
+ *
  * A. A. Nielsen and K. Conradsen, "Multivariate alteration detection
  * (mad) in multispectral, bi-temporal image data: a new approach to
  * change detection studies," Remote Sens. Environ., vol. 64,
  * pp. 1-19, (1998)
- * 
+ *
  * Multivariate Alteration Detector takes two images as inputs and
  * produce a set of N change maps as a VectorImage (where N is the
  * maximum of number of bands in first and second image) with the
  * following properties:
- * 
+ *
  * - Change maps are differences of a pair of linear combinations of
  * bands from image 1 and bands from image 2 chosen to maximize the
  * correlation.
  * - Each change map is orthogonal to the others.
- * 
+ *
  * This is a statistical method which can handle different modalities
  * and even differents bands and number of bands between images.
- * 
+ *
  * If numbers of bands in image 1 and 2 are equal, then change maps
  * are sorted by increasing correlation. If number of bands is
  * different, the change maps are sorted by decreasing correlation.
- * 
+ *
  * The GetV1() and GetV2() methods allow to retrieve the linear
  * combinations used to generate the Mad change maps as a vnl_matrix of
  * double, and the GetRho() method allows to retrieve the correlation
  * associated to each Mad change maps as a vnl_vector.
- * 
+ *
  * This filter has been implemented from the Matlab code kindly made
  * available by the authors here:
  * http://www2.imm.dtu.dk/~aa/software.html
- * 
+ *
  * Both cases (same and different number of bands) have been validated
  * by comparing the output image to the output produced by the Matlab
  * code, and the reference images for testing have been generated from
@@ -74,8 +74,8 @@ namespace otb
  * \ingroup Streamed, Multithreaded
  */
 template <class TInputImage, class TOutputImage>
-class ITK_EXPORT MultivariateAlterationDetectorImageFilter 
-  : public itk::ImageToImageFilter<TInputImage,TOutputImage>
+class ITK_EXPORT MultivariateAlterationDetectorImageFilter
+  : public itk::ImageToImageFilter<TInputImage, TOutputImage>
 {
 public:
   /** Standard class typedefs. */
@@ -104,7 +104,7 @@ public:
   /** Internal filters types */
   typedef StreamingStatisticsVectorImageFilter<InputImageType> CovarianceEstimatorType;
   typedef typename CovarianceEstimatorType::Pointer            CovarianceEstimatorPointer;
-  typedef otb::ConcatenateVectorImageFilter<InputImageType,InputImageType,InputImageType> ConcatenateImageFilterType;
+  typedef otb::ConcatenateVectorImageFilter<InputImageType, InputImageType, InputImageType> ConcatenateImageFilterType;
   typedef typename ConcatenateImageFilterType::Pointer         ConcatenateImageFilterPointer;
 
   typedef typename CovarianceEstimatorType::MatrixObjectType   MatrixObjectType;
@@ -120,18 +120,18 @@ public:
   /** Get the linear combinations of bands of image 1 associated to
    *  multivariate alteration detector. This is a square matrix of
    *  size nbBand of image 1. */
-  itkGetMacro(V1,VnlMatrixType);
+  itkGetMacro(V1, VnlMatrixType);
 
   /** Get the linear combinations of bands of image 2 associated to
    *  multivariate alteration detector. This is a square matrix of
    *  size nbBand of image 2. */
-  itkGetMacro(V2,VnlMatrixType);
+  itkGetMacro(V2, VnlMatrixType);
 
   /** Get the correlation coefficient associated with each mad.*/
-  itkGetMacro(Rho,VnlVectorType);
+  itkGetMacro(Rho, VnlVectorType);
 
   /** Get the covariance estimator (for progress reporting purposes) */
-  itkGetObjectMacro(CovarianceEstimator,CovarianceEstimatorType);
+  itkGetObjectMacro(CovarianceEstimator, CovarianceEstimatorType);
   
   /** Connect one of the operands for pixel-wise addition */
   void SetInput1(const TInputImage * image1);
@@ -147,7 +147,7 @@ protected:
   MultivariateAlterationDetectorImageFilter();
   virtual ~MultivariateAlterationDetectorImageFilter() {}
 
-  virtual void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread,int threadId);
+  virtual void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId);
 
   virtual void GenerateOutputInformation();
 
diff --git a/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.txx b/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.txx
index 4bd15242185ef5e29ef70468a6ca19bfac17f33d..7c06d5905e27a1d4d7211d34b3bf74640850b28d 100644
--- a/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.txx
+++ b/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.txx
@@ -30,7 +30,7 @@
 namespace otb
 {
 template <class TInputImage, class TOutputImage>
-MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
+MultivariateAlterationDetectorImageFilter<TInputImage, TOutputImage>
 ::MultivariateAlterationDetectorImageFilter()
 {
   this->SetNumberOfRequiredInputs(2);
@@ -39,7 +39,7 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
 
 template <class TInputImage, class TOutputImage>
 void
-MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
+MultivariateAlterationDetectorImageFilter<TInputImage, TOutputImage>
 ::SetInput1(const TInputImage * image1)
 {
   // Process object is not const-correct so the const casting is required.
@@ -47,9 +47,9 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
 }
 
 template <class TInputImage, class TOutputImage>
-const typename MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
+const typename MultivariateAlterationDetectorImageFilter<TInputImage, TOutputImage>
 ::InputImageType *
-MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
+MultivariateAlterationDetectorImageFilter<TInputImage, TOutputImage>
 ::GetInput1()
 {
   if (this->GetNumberOfInputs() < 1)
@@ -61,7 +61,7 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
 
 template <class TInputImage, class TOutputImage>
 void
-MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
+MultivariateAlterationDetectorImageFilter<TInputImage, TOutputImage>
 ::SetInput2(const TInputImage * image2)
 {
   // Process object is not const-correct so the const casting is required.
@@ -69,9 +69,9 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
 }
 
 template <class TInputImage, class TOutputImage>
-const typename MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
+const typename MultivariateAlterationDetectorImageFilter<TInputImage, TOutputImage>
 ::InputImageType *
-MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
+MultivariateAlterationDetectorImageFilter<TInputImage, TOutputImage>
 ::GetInput2()
 {
   if (this->GetNumberOfInputs() < 2)
@@ -84,7 +84,7 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
 
 template <class TInputImage, class TOutputImage>
 void
-MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
+MultivariateAlterationDetectorImageFilter<TInputImage, TOutputImage>
 ::GenerateOutputInformation()
 {
   // Call superclass implementation
@@ -98,7 +98,7 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
   // Get the number of components for each image
   unsigned int nbComp1 = input1Ptr->GetNumberOfComponentsPerPixel();
   unsigned int nbComp2 = input2Ptr->GetNumberOfComponentsPerPixel();
-  unsigned int outNbComp = std::max(nbComp1,nbComp2);
+  unsigned int outNbComp = std::max(nbComp1, nbComp2);
 
   outputPtr->SetNumberOfComponentsPerPixel(outNbComp);
 
@@ -120,21 +120,21 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
   m_MeanValues = m_CovarianceEstimator->GetMean();
 
   // Extract sub-matrices of the covariance matrix
-  VnlMatrixType s11 = m_CovarianceMatrix.GetVnlMatrix().extract(nbComp1,nbComp1);
-  VnlMatrixType s22 = m_CovarianceMatrix.GetVnlMatrix().extract(nbComp2,nbComp2,nbComp1,nbComp1);
-  VnlMatrixType s12 = m_CovarianceMatrix.GetVnlMatrix().extract(nbComp1,nbComp2,0,nbComp1);
+  VnlMatrixType s11 = m_CovarianceMatrix.GetVnlMatrix().extract(nbComp1, nbComp1);
+  VnlMatrixType s22 = m_CovarianceMatrix.GetVnlMatrix().extract(nbComp2, nbComp2, nbComp1, nbComp1);
+  VnlMatrixType s12 = m_CovarianceMatrix.GetVnlMatrix().extract(nbComp1, nbComp2, 0, nbComp1);
   VnlMatrixType s21 = s12.transpose();
 
   // Extract means
-  m_Mean1 = VnlVectorType(nbComp1,0);
-  m_Mean2 = VnlVectorType(nbComp2,0);
+  m_Mean1 = VnlVectorType(nbComp1, 0);
+  m_Mean2 = VnlVectorType(nbComp2, 0);
 
-  for(unsigned int i = 0; i<nbComp1;++i)
+  for(unsigned int i = 0; i<nbComp1; ++i)
     {
     m_Mean1[i] = m_MeanValues[i];
     }
 
-  for(unsigned int i = 0; i<nbComp2;++i)
+  for(unsigned int i = 0; i<nbComp2; ++i)
     {
     m_Mean2[i] = m_MeanValues[nbComp1+i];
     }
@@ -148,7 +148,7 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
     // Build the generalized eigensystem
     VnlMatrixType s12s22is21 = s12 * invs22 *s21;
     
-    vnl_generalized_eigensystem ges(s12s22is21,s11);
+    vnl_generalized_eigensystem ges(s12s22is21, s11);
 
     m_V1 = ges.V;
 
@@ -165,13 +165,13 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
     invstderr1.fill(0);
     invstderr1.set_diagonal(diag1);
 
-    VnlMatrixType sign1 = VnlMatrixType(nbComp1,nbComp1,0);
+    VnlMatrixType sign1 = VnlMatrixType(nbComp1, nbComp1, 0);
 
     VnlMatrixType aux4 = invstderr1 * s11 * m_V1;
 
-    VnlVectorType aux5 = VnlVectorType(nbComp1,0);
+    VnlVectorType aux5 = VnlVectorType(nbComp1, 0);
 
-    for(unsigned int i = 0; i < nbComp1;++i)
+    for(unsigned int i = 0; i < nbComp1; ++i)
       {
       aux5=aux5 + aux4.get_row(i);
       }
@@ -188,40 +188,40 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
     VnlVectorType aux2 = aux1.get_diagonal();
     aux2 = aux2.apply(&vcl_sqrt);
     aux2 = aux2.apply(&InverseValue);
-    VnlMatrixType aux3 = VnlMatrixType(aux2.size(),aux2.size(),0);
+    VnlMatrixType aux3 = VnlMatrixType(aux2.size(), aux2.size(), 0);
     aux3.fill(0);
     aux3.set_diagonal(aux2);
     m_V2 =  m_V2 * aux3;
     }
   else
     {
-    VnlMatrixType sl(nbComp1+nbComp2,nbComp1+nbComp2,0);
-    VnlMatrixType sr(nbComp1+nbComp2,nbComp1+nbComp2,0);
+    VnlMatrixType sl(nbComp1+nbComp2, nbComp1+nbComp2, 0);
+    VnlMatrixType sr(nbComp1+nbComp2, nbComp1+nbComp2, 0);
 
-    sl.update(s12,0,nbComp1);
-    sl.update(s21,nbComp1,0);
-    sr.update(s11,0,0);
-    sr.update(s22,nbComp1,nbComp1);
+    sl.update(s12, 0, nbComp1);
+    sl.update(s21, nbComp1, 0);
+    sr.update(s11, 0, 0);
+    sr.update(s22, nbComp1, nbComp1);
 
-    vnl_generalized_eigensystem ges(sl,sr);
+    vnl_generalized_eigensystem ges(sl, sr);
 
     VnlMatrixType V = ges.V;
     
     V.fliplr();
 
-    m_V1 = V.extract(nbComp1,nbComp1);
-    m_V2 = V.extract(nbComp2,nbComp2,nbComp1,0);
+    m_V1 = V.extract(nbComp1, nbComp1);
+    m_V2 = V.extract(nbComp2, nbComp2, nbComp1, 0);
 
-    m_Rho = ges.D.get_diagonal().flip().extract(std::max(nbComp1,nbComp2),0);
+    m_Rho = ges.D.get_diagonal().flip().extract(std::max(nbComp1, nbComp2), 0);
 
     //Scale v1 to get a unit variance
     VnlMatrixType aux1 = m_V1.transpose() * (s11 * m_V1);
 
     VnlVectorType aux2 = aux1.get_diagonal();
     aux2 = aux2.apply(&vcl_sqrt);
-    aux2 = aux2.apply(&InverseValue);    
+    aux2 = aux2.apply(&InverseValue);
 
-    VnlMatrixType aux3 = VnlMatrixType(aux2.size(),aux2.size(),0);
+    VnlMatrixType aux3 = VnlMatrixType(aux2.size(), aux2.size(), 0);
     aux3.set_diagonal(aux2);
     m_V1 = m_V1 * aux3;
 
@@ -231,13 +231,13 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
     invstderr1.fill(0);
     invstderr1.set_diagonal(diag1);
 
-    VnlMatrixType sign1 = VnlMatrixType(nbComp1,nbComp1,0);
+    VnlMatrixType sign1 = VnlMatrixType(nbComp1, nbComp1, 0);
 
     VnlMatrixType aux4 = invstderr1 * s11 * m_V1;
 
-    VnlVectorType aux5 = VnlVectorType(nbComp1,0);
+    VnlVectorType aux5 = VnlVectorType(nbComp1, 0);
 
-    for(unsigned int i = 0; i < nbComp1;++i)
+    for(unsigned int i = 0; i < nbComp1; ++i)
       {
       aux5=aux5 + aux4.get_row(i);
       }
@@ -252,12 +252,12 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
     aux2 = aux1.get_diagonal();
     aux2 = aux2.apply(&vcl_sqrt);
     aux2 = aux2.apply(&InverseValue);
-    aux3 = VnlMatrixType(aux2.size(),aux2.size(),0);
+    aux3 = VnlMatrixType(aux2.size(), aux2.size(), 0);
     aux3.fill(0);
     aux3.set_diagonal(aux2);
     m_V2 =  m_V2 * aux3;
 
-    VnlMatrixType sign2 = VnlMatrixType(nbComp2,nbComp2,0);
+    VnlMatrixType sign2 = VnlMatrixType(nbComp2, nbComp2, 0);
     
     aux5 = (m_V1.transpose() * s12 * m_V2).transpose().get_diagonal();
     sign2.set_diagonal(aux5);
@@ -268,7 +268,7 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
 
 template <class TInputImage, class TOutputImage>
 void
-MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
+MultivariateAlterationDetectorImageFilter<TInputImage, TOutputImage>
 ::ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId)
 {
   // Retrieve input images pointers
@@ -280,7 +280,7 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
   typedef itk::ImageRegionConstIterator<InputImageType>  ConstIteratorType;
   typedef itk::ImageRegionIterator<OutputImageType> IteratorType;
 
-  IteratorType outIt(outputPtr,outputRegionForThread);
+  IteratorType outIt(outputPtr, outputRegionForThread);
   ConstIteratorType inIt1(input1Ptr, outputRegionForThread);
   ConstIteratorType inIt2(input2Ptr, outputRegionForThread);
 
@@ -298,18 +298,18 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
 
   while(!inIt1.IsAtEnd() && !inIt2.IsAtEnd() && !outIt.IsAtEnd())
     {
-    VnlVectorType x1(nbComp1,0);
-    VnlVectorType x1bis(outNbComp,0);
-    VnlVectorType x2(nbComp2,0);
-    VnlVectorType x2bis(outNbComp,0);
-    VnlVectorType mad(outNbComp,0);
+    VnlVectorType x1(nbComp1, 0);
+    VnlVectorType x1bis(outNbComp, 0);
+    VnlVectorType x2(nbComp2, 0);
+    VnlVectorType x2bis(outNbComp, 0);
+    VnlVectorType mad(outNbComp, 0);
     
-    for(unsigned int i = 0; i < nbComp1;++i)
+    for(unsigned int i = 0; i < nbComp1; ++i)
       {
       x1[i] = inIt1.Get()[i];
       }
     
-    for(unsigned int i = 0; i < nbComp2;++i)
+    for(unsigned int i = 0; i < nbComp2; ++i)
       {
       x2[i] = inIt2.Get()[i];
       }
@@ -317,12 +317,12 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
     VnlVectorType first = (x1-m_Mean1)*m_V1;
     VnlVectorType second = (x2-m_Mean2)*m_V2;
 
-    for(unsigned int i = 0; i < nbComp1;++i)
+    for(unsigned int i = 0; i < nbComp1; ++i)
       {
       x1bis[i] = first[i];
       }
     
-    for(unsigned int i = 0; i < nbComp2;++i)
+    for(unsigned int i = 0; i < nbComp2; ++i)
       {
       x2bis[i] = second[i];
       }
@@ -344,7 +344,7 @@ MultivariateAlterationDetectorImageFilter<TInputImage,TOutputImage>
         {
         outPixel[i]=mad[outNbComp - i - 1];
 
-        if(i < outNbComp - std::min(nbComp1,nbComp2))
+        if(i < outNbComp - std::min(nbComp1, nbComp2))
           {
           outPixel[i]*=vcl_sqrt(2.);
           }
diff --git a/Testing/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.cxx b/Testing/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.cxx
index c8b598f4b4b2d13cf25d70a251ede6d4d63c9329..e41cbbeb89214645b70e85a0a160f2743fe3c9f6 100644
--- a/Testing/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.cxx
+++ b/Testing/Code/BasicFilters/otbMaximumAutocorrelationFactorImageFilter.cxx
@@ -20,11 +20,11 @@
 #include "otbStreamingImageFileWriter.h"
 #include "otbMaximumAutocorrelationFactorImageFilter.h"
 
-typedef otb::VectorImage<unsigned short,2> ImageType;
-typedef otb::VectorImage<double,2>         OutputImageType;
+typedef otb::VectorImage<unsigned short, 2> ImageType;
+typedef otb::VectorImage<double, 2>         OutputImageType;
 typedef otb::ImageFileReader<ImageType>    ReaderType;
 typedef otb::StreamingImageFileWriter<OutputImageType> WriterType;
-typedef otb::MaximumAutocorrelationFactorImageFilter<ImageType,OutputImageType> MADFilterType;
+typedef otb::MaximumAutocorrelationFactorImageFilter<ImageType, OutputImageType> MADFilterType;
 
 int otbMaximumAutocorrelationFactorImageFilterNew(int argc, char* argv[])
 {
diff --git a/Testing/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.cxx b/Testing/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.cxx
index cfb8700f6c7d6af3ad680a5ac4085976ce575c66..c0eb11240475a92e6a470b4e95435074db9f9281 100644
--- a/Testing/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.cxx
+++ b/Testing/Code/ChangeDetection/otbMultivariateAlterationDetectorImageFilter.cxx
@@ -20,11 +20,11 @@
 #include "otbImageFileWriter.h"
 #include "otbMultivariateAlterationDetectorImageFilter.h"
 
-typedef otb::VectorImage<unsigned short,2> ImageType;
-typedef otb::VectorImage<double,2>         OutputImageType;
+typedef otb::VectorImage<unsigned short, 2> ImageType;
+typedef otb::VectorImage<double, 2>         OutputImageType;
 typedef otb::ImageFileReader<ImageType>    ReaderType;
 typedef otb::ImageFileWriter<OutputImageType> WriterType;
-typedef otb::MultivariateAlterationDetectorImageFilter<ImageType,OutputImageType> MADFilterType;
+typedef otb::MultivariateAlterationDetectorImageFilter<ImageType, OutputImageType> MADFilterType;
 
 
 int otbMultivariateAlterationDetectorImageFilterNew(int argc, char* argv[])