DOC: review example MultivariateAlterationDetector

Examples/ChangeDetection/MultivariateAlterationDetector.cxx

@@ -19,12 +19,6 @@
  */
 
 
-#include "otbVectorImage.h"
-#include "otbImageFileReader.h"
-#include "otbImageFileWriter.h"
-#include "otbPrintableImageFilter.h"
-
-
 /* Example usage:
 ./MultivariateAlterationDetector Input/Spot5-Gloucester-before.tif \
                                  Input/Spot5-Gloucester-after.tif \
@@ -34,33 +28,10 @@
                                  Output/mad-output.png
 */
 
-
-// This example illustrates the class
-// \doxygen{otb}{MultivariateAlterationChangeDetectorImageFilter},
-// which implements the Multivariate Alteration Change Detector
-// algorithm \cite{nielsen2007regularized}. This algorihtm allows
-// performing change detection from a pair multi-band images, including
-// images with different number of bands or modalities. Its output is
-// a a multi-band image of change maps, each one being unccorrelated
-// with the remaining. The number of bands of the output image is the
-// minimum number of bands between the two input images.
-//
-// The algorithm works as follows. It tries to find two linear
-// combinations of bands (one for each input images) which maximize
-// correlation, and subtract these two linear combinitation, leading
-// to the first change map. Then, it looks for a second set of linear
-// combinations which are orthogonal to the first ones, a which
-// maximize correlation, and use it as the second change map. This
-// process is iterated until no more orthogonal linear combinations
-// can be found.
-//
-// This algorithms has numerous advantages, such as radiometry scaling
-// and shifting invariance and absence of parameters, but it can not
-// be used on a pair of single band images (in this case the output is
-// simply the difference between the two images).
-//
-// We start by including the corresponding header file.
-
+#include "otbVectorImage.h"
+#include "otbImageFileReader.h"
+#include "otbImageFileWriter.h"
+#include "otbPrintableImageFilter.h"
 #include "otbMultivariateAlterationDetectorImageFilter.h"
 
 int main(int argc, char* argv[])
@@ -79,36 +50,31 @@ int main(int argc, char* argv[])
 
   // We then define the types for the input images and for the
   // change image.
-
   using InputPixelType  = unsigned short;
   using OutputPixelType = float;
   using InputImageType  = otb::VectorImage<InputPixelType, Dimension>;
   using OutputImageType = otb::VectorImage<OutputPixelType, Dimension>;
 
-  //  We can now declare the types for the reader. Since the images
-  //  can be vey large, we will force the pipeline to use
-  //  streaming. For this purpose, the file writer will be
-  //  streamed. This is achieved by using the
-  //  \doxygen{otb}{ImageFileWriter} class.
-
+  // We can now declare the types for the reader. Since the images
+  // can be vey large, we will force the pipeline to use
+  // streaming. For this purpose, the file writer will be
+  // streamed. This is achieved by using the
+  // ImageFileWriter class.
   using ReaderType = otb::ImageFileReader<InputImageType>;
   using WriterType = otb::ImageFileWriter<OutputImageType>;
 
-
   // This is for rendering in software guide
   using InputPrintFilterType  = otb::PrintableImageFilter<InputImageType, InputImageType>;
   using OutputPrintFilterType = otb::PrintableImageFilter<OutputImageType, OutputImageType>;
   using VisuImageType         = InputPrintFilterType::OutputImageType;
   using VisuWriterType        = otb::ImageFileWriter<VisuImageType>;
 
-  //  The \doxygen{otb}{MultivariateAlterationDetectorImageFilter} is templated over
-  //  the type of the input images and the type of the generated change
-  //  image.
-
+  // The MultivariateAlterationDetectorImageFilter is templated over
+  // the type of the input images and the type of the generated change
+  // image.
   using MADFilterType = otb::MultivariateAlterationDetectorImageFilter<InputImageType, OutputImageType>;
 
-  //  The different elements of the pipeline can now be instantiated.
-
+  // The different elements of the pipeline can now be instantiated.
   ReaderType::Pointer    reader1   = ReaderType::New();
   ReaderType::Pointer    reader2   = ReaderType::New();
   WriterType::Pointer    writer    = WriterType::New();
@@ -120,30 +86,19 @@ int main(int argc, char* argv[])
   const char* in1pretty      = argv[4];
   const char* in2pretty      = argv[5];
   const char* outpretty      = argv[6];
-  //  We set the parameters of the different elements of the pipeline.
 
+  // We set the parameters of the different elements of the pipeline.
   reader1->SetFileName(inputFilename1);
   reader2->SetFileName(inputFilename2);
   writer->SetFileName(outputFilename);
 
-  //  We build the pipeline by plugging all the elements together.
-
+  // We build the pipeline by plugging all the elements together.
   madFilter->SetInput1(reader1->GetOutput());
   madFilter->SetInput2(reader2->GetOutput());
   writer->SetInput(madFilter->GetOutput());
 
-  try
-  {
-    //  And then we can trigger the pipeline update, as usual.
-
-    writer->Update();
-  }
-  catch (itk::ExceptionObject& err)
-  {
-    std::cout << "ExceptionObject caught !" << std::endl;
-    std::cout << err << std::endl;
-    return -1;
-  }
+  // And then we can trigger the pipeline update, as usual.
+  writer->Update();
 
   // Here we generate the figures
   InputPrintFilterType::Pointer  input1PrintFilter = InputPrintFilterType::New();
@@ -177,18 +132,4 @@ int main(int argc, char* argv[])
   input1VisuWriter->Update();
   input2VisuWriter->Update();
   outputVisuWriter->Update();
-
-  // Figure \ref{fig:MADCHDET} shows the
-  // results of Multivariate Alteration Detector applied to a pair of
-  // SPOT5 images before and after a flooding event.
-  // \begin{figure}
-  // \center \includegraphics[width=0.32\textwidth]{mad-input1.eps}
-  // \includegraphics[width=0.32\textwidth]{mad-input2.eps}
-  // \includegraphics[width=0.32\textwidth]{mad-output.eps}
-  // \itkcaption[Multivariate Alteration Detection
-  // Results]{Result of the Multivariate Alteration Detector results on
-  // SPOT5 data before and after flooding.}  \label{fig:MADCHDET}
-  // \end{figure}
-
-  return EXIT_SUCCESS;
 }

Examples/ChangeDetection/MultivariateAlterationDetector.rst

+This example illustrates the class
+:doxygen:`MultivariateAlterationDetectorImageFilter`,
+which implements the Multivariate Alteration Change Detector
+algorithm. This algorihtm allows
+performing change detection from a pair multi-band images, including
+images with different number of bands or modalities. Its output is
+a multi-band image of change maps, each one being unccorrelated
+with the remaining. The number of bands of the output image is the
+minimum number of bands between the two input images.
+
+The algorithm works as follows. It tries to find two linear
+combinations of bands (one for each input images) which maximize
+correlation, and subtract these two linear combinitation, leading
+to the first change map. Then, it looks for a second set of linear
+combinations which are orthogonal to the first ones, a which
+maximize correlation, and use it as the second change map. This
+process is iterated until no more orthogonal linear combinations
+can be found.
+
+This algorithms has numerous advantages, such as radiometry scaling
+and shifting invariance and absence of parameters, but it can not
+be used on a pair of single band images (in this case the output is
+simply the difference between the two images).
+
+.. |image1| image:: /Output/mad-input2.png
+
+.. |image2| image:: /Output/mad-output.png
+
+.. _Figure1:
+
++--------------------------+-------------------------+
+|        |image1|          |         |image2|        |
++--------------------------+-------------------------+
+
+    Result of the Multivariate Alteration Detector results on SPOT5 data before and after flooding.