/usr/include/OTB-6.4/otbEigenvalueLikelihoodMaximisation.txx is in libotb-dev 6.4.0+dfsg-1.
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* Copyright (C) 1999-2011 Insight Software Consortium
* Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
*
* 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.
*/
#ifndef otbEigenvalueLikelihoodMaximisation_txx
#define otbEigenvalueLikelihoodMaximisation_txx
#include "otbEigenvalueLikelihoodMaximisation.h"
#include <vcl_algorithm.h>
namespace otb
{
template<class TPrecision>
EigenvalueLikelihoodMaximisation<TPrecision>
::EigenvalueLikelihoodMaximisation()
: m_NumberOfPixels(0),
m_NumberOfEndmembers(0)
{
}
template<class TInputImage>
void
EigenvalueLikelihoodMaximisation<TInputImage>
::Compute()
{
// TODO check size
const unsigned int nbBands = m_Covariance.rows();
// Compute diagonalisation of covariance and correlation
vnl_symmetric_eigensystem<PrecisionType> eigenK(m_Covariance);
VectorType eigenCovariance = eigenK.D.diagonal();
vcl_sort(eigenCovariance.begin(), eigenCovariance.end());
eigenCovariance.flip();
vnl_symmetric_eigensystem<PrecisionType> eigenR(m_Correlation);
VectorType eigenCorrelation = eigenR.D.diagonal();
vcl_sort(eigenCorrelation.begin(), eigenCorrelation.end());
eigenCorrelation.flip();
// Compute likelihood log
m_Likelihood.set_size(nbBands);
const double coef = 2.0/m_NumberOfPixels;
for(unsigned int i=0; i < nbBands; ++i)
{
const unsigned int nl = nbBands - i;
VectorType sigma(nl), t(nl);
for (unsigned int j = 0; j < nl; ++j )
{
PrecisionType r = eigenCorrelation[j + i];
PrecisionType k = eigenCovariance[j + i];
sigma[j] = coef * (r * r + k * k);
//std::cout << "sigma[" << j << "]=" << sigma[j] << std::endl;
t[j] = (r - k) * (r - k) / sigma[j];
//std::cout << "t[" << j <<"]=" << t[j] << std::endl;
sigma[j] = vcl_log(sigma[j]);
}
m_Likelihood(i) = -0.5*t.sum() - 0.5*sigma.sum();
}
// Extract first local maximum
//double max = m_Likelihood[0];
unsigned int iMax = 0;
for (unsigned int i = 1; i < m_Likelihood.size() - 1; ++i)
{
if ( (m_Likelihood[i] > m_Likelihood[i - 1])
&& (m_Likelihood[i] > m_Likelihood[i + 1]) )
{
//max = m_Likelihood[i];
iMax = i;
break;
}
}
m_NumberOfEndmembers = iMax;
}
template <class TImage>
void
EigenvalueLikelihoodMaximisation<TImage>
::PrintSelf(std::ostream& os, itk::Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "Covariance: " << m_Covariance << std::endl;
os << indent << "Correlation: " << m_Correlation << std::endl;
os << indent << "NumberOfEndmembers: " << m_NumberOfEndmembers << std::endl;
os << indent << "Likelihood: " << m_Likelihood << std::endl;
}
} // end namespace otb
#endif
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