MaximumAutocorrelationFactor.cxx 4.95 KB
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/*
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 * Copyright (C) 2005-2019 Centre National d'Etudes Spatiales (CNES)
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 *
 * This file is part of Orfeo Toolbox
 *
 *     https://www.orfeo-toolbox.org/
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
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#include "otbVectorImage.h"
#include "otbImageFileReader.h"
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#include "otbImageFileWriter.h"
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#include "otbPrintableImageFilter.h"

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/* Example usage:
./MaximumAutocorrelationFactor Input/wv2_cannes_8bands.tif Output/MAFOutput.tif Output/maf-input.png Output/maf-output.png
*/

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// This example illustrates the class
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// \doxygen{otb}{MaximumAutocorrelationFactorImageFilter}, which
// performs a Maximum Autocorrelation Factor transform \cite{nielsen2011kernel}. Like
// PCA, MAF tries to find a set of orthogonal linear transform, but
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// the criterion to maximize is the spatial auto-correlation rather than the
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// variance.
//
// Auto-correlation is the correlation between the component and a
// unitary shifted version of the component.
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//
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// Please note that the inverse transform is not implemented yet.
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//
// We start by including the corresponding header file.

#include "otbMaximumAutocorrelationFactorImageFilter.h"

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int main(int itkNotUsed(argc), char* argv[])
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{
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  char* infname   = argv[1];
  char* outfname  = argv[2];
  char* inpretty  = argv[3];
  char* outpretty = argv[4];
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  //  We then define the types for the input image and the
  //  output image.
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  using InputImageType  = otb::VectorImage<unsigned short, 2>;
  using OutputImageType = otb::VectorImage<double, 2>;
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  //  We can now declare the types for the reader. Since the images
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  //  can be very large, we will force the pipeline to use
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  //  streaming. For this purpose, the file writer will be
  //  streamed. This is achieved by using the
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  //  \doxygen{otb}{ImageFileWriter} class.
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  using ReaderType = otb::ImageFileReader<InputImageType>;
  using WriterType = otb::ImageFileWriter<OutputImageType>;
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  //  The \doxygen{otb}{MultivariateAlterationDetectorImageFilter} is templated over
  //  the type of the input images and the type of the generated change
  //  image.

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  using FilterType = otb::MaximumAutocorrelationFactorImageFilter<InputImageType, OutputImageType>;
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  //  The different elements of the pipeline can now be instantiated.

  ReaderType::Pointer reader = ReaderType::New();
  WriterType::Pointer writer = WriterType::New();
  FilterType::Pointer filter = FilterType::New();

  //  We set the parameters of the different elements of the pipeline.

  reader->SetFileName(infname);
  writer->SetFileName(outfname);

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

  filter->SetInput(reader->GetOutput());
  writer->SetInput(filter->GetOutput());

  //  And then we can trigger the pipeline update, as usual.
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  writer->Update();

  // This is for rendering in software guide
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  using InputPrintFilterType  = otb::PrintableImageFilter<InputImageType, InputImageType>;
  using OutputPrintFilterType = otb::PrintableImageFilter<OutputImageType, OutputImageType>;
  using VisuImageType         = InputPrintFilterType::OutputImageType;
  using VisuWriterType        = otb::ImageFileWriter<VisuImageType>;
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  InputPrintFilterType::Pointer  inputPrintFilter  = InputPrintFilterType::New();
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  OutputPrintFilterType::Pointer outputPrintFilter = OutputPrintFilterType::New();
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  VisuWriterType::Pointer        inputVisuWriter   = VisuWriterType::New();
  VisuWriterType::Pointer        outputVisuWriter  = VisuWriterType::New();
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  inputPrintFilter->SetInput(reader->GetOutput());
  inputPrintFilter->SetChannel(5);
  inputPrintFilter->SetChannel(3);
  inputPrintFilter->SetChannel(2);
  outputPrintFilter->SetInput(filter->GetOutput());
  outputPrintFilter->SetChannel(1);
  outputPrintFilter->SetChannel(2);
  outputPrintFilter->SetChannel(3);

  inputVisuWriter->SetInput(inputPrintFilter->GetOutput());
  outputVisuWriter->SetInput(outputPrintFilter->GetOutput());

  inputVisuWriter->SetFileName(inpretty);
  outputVisuWriter->SetFileName(outpretty);

  inputVisuWriter->Update();
  outputVisuWriter->Update();

  // Figure \ref{fig:MAFFIG} shows the
  // results of Maximum Autocorrelation Factor applied to an 8 bands
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  // Worldview2 image.
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  // \begin{figure}
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  // \center \includegraphics[width=0.32\textwidth]{maf-input.eps}
  // \includegraphics[width=0.32\textwidth]{maf-output.eps}
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  // \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}
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  // \end{figure}
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  return EXIT_SUCCESS;
}