CorrelChDet.cxx

Example usage:

./CorrelChDet Input/ERSBefore.png Input/ERSAfter.png Output/CorrChDet.tif 15

Example source code (CorrelChDet.cxx):

#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "otbImage.h"
#include "itkShiftScaleImageFilter.h"
#include "otbCommandProgressUpdate.h"




// This example illustrates the class
// \doxygen{otb}{CorrelationChangeDetector} for detecting changes
// between pairs of images. This filter computes the correlation coefficient in
// the neighborhood of each pixel of the pair of images to be compared. This
// example will use the images shown in
// figure ~\ref{fig:CORRCHDETINIM}. These correspond to two ERS acquisitions before and during a flood.
// \begin{figure}
// \center
// \includegraphics[width=0.35\textwidth]{ERSBefore.eps}
// \includegraphics[width=0.35\textwidth]{ERSAfter.eps}
// \itkcaption[ERS Images for Change Detection]{Images used for the
// change detection. Left: Before the flood. Right: during the flood.}
// \label{fig:CORRCHDETINIM}
// \end{figure}
//
// We start by including the corresponding header file.

#include "otbCorrelationChangeDetector.h"

int main(int argc, char* argv[])
{

  if (argc < 5)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << " inputImageFile1 inputImageFile2 "
              << "outputImageFile radius" << std::endl;
    return -1;
  }

  // Define the dimension of the images
  const unsigned int Dimension = 2;

  // We start by declaring the types for the two input images, the
  // change image and the image to be stored in a file for visualization.

  typedef float                                    InternalPixelType;
  typedef unsigned char                            OutputPixelType;
  typedef otb::Image<InternalPixelType, Dimension> InputImageType1;
  typedef otb::Image<InternalPixelType, Dimension> InputImageType2;
  typedef otb::Image<InternalPixelType, Dimension> ChangeImageType;
  typedef otb::Image<OutputPixelType, Dimension>   OutputImageType;

  //  We can now declare the types for the readers. 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.

  typedef otb::ImageFileReader<InputImageType1> ReaderType1;
  typedef otb::ImageFileReader<InputImageType2> ReaderType2;
  typedef otb::ImageFileWriter<OutputImageType> WriterType;

  //  The change detector will give a response which is normalized
  //  between 0 and 1. Before
  //  saving the image to a file in, for instance, PNG format, we will
  //  rescale the results of the change detection in order to use all
  //  the output pixel type range of values.

  typedef itk::ShiftScaleImageFilter<ChangeImageType, OutputImageType> RescalerType;

  //  The \doxygen{otb}{CorrelationChangeDetector} is templated over
  //  the types of the two input images and the type of the generated change
  //  image.

  typedef otb::CorrelationChangeDetector<InputImageType1, InputImageType2, ChangeImageType> FilterType;

  //  The different elements of the pipeline can now be instantiated.

  ReaderType1::Pointer  reader1        = ReaderType1::New();
  ReaderType2::Pointer  reader2        = ReaderType2::New();
  WriterType::Pointer   writer         = WriterType::New();
  FilterType::Pointer   filter         = FilterType::New();
  RescalerType::Pointer rescaler       = RescalerType::New();
  const char*           inputFilename1 = argv[1];
  const char*           inputFilename2 = argv[2];
  const char*           outputFilename = argv[3];
  //  We set the parameters of the different elements of the pipeline.

  reader1->SetFileName(inputFilename1);
  reader2->SetFileName(inputFilename2);
  writer->SetFileName(outputFilename);

  float scale = itk::NumericTraits<OutputPixelType>::max();
  rescaler->SetScale(scale);

  //  The only parameter for this change detector is the radius of
  //  the window used for computing the correlation coefficient.

  filter->SetRadius(atoi(argv[4]));

  //  We build the pipeline by plugging all the elements together.

  filter->SetInput1(reader1->GetOutput());
  filter->SetInput2(reader2->GetOutput());
  rescaler->SetInput(filter->GetOutput());
  writer->SetInput(rescaler->GetOutput());

  //  Since the processing time of large images can be long, it is
  //  interesting to monitor the evolution of the computation. In
  //  order to do so, the change detectors can use the
  //  command/observer design pattern. This is easily done by
  //  attaching an observer to the filter.

  typedef otb::CommandProgressUpdate<FilterType> CommandType;

  CommandType::Pointer observer = CommandType::New();
  filter->AddObserver(itk::ProgressEvent(), observer);

  try
  {
    writer->Update();
  }
  catch (itk::ExceptionObject& err)
  {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return -1;
  }

  // Figure \ref{fig:RESCORRCHDET} shows the result of the change
  // detection by local correlation.
  // \begin{figure}
  // \center
  // \includegraphics[width=0.35\textwidth]{CorrChDet.eps}
  // \itkcaption[Correlation Change Detection Results]{Result of the
  // correlation change detector}
  // \label{fig:RESCORRCHDET}
  // \end{figure}

  return EXIT_SUCCESS;
}