/usr/include/OTB-5.8/otbEigenvalueLikelihoodMaximisation.txx is in libotb-dev 5.8.0+dfsg-3.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | /*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt for details.
Some parts of this code are derived from ITK. See ITKCopyright.txt
for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#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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