/usr/include/OTB-6.4/otbVirtualDimensionality.txx is in libotb-dev 6.4.0+dfsg-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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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 otbVirtualDimensionality_txx
#define otbVirtualDimensionality_txx
#include "otbVirtualDimensionality.h"
#include <vcl_algorithm.h>
#include "otb_boost_math_normal_header.h"
namespace otb
{
template<class TPrecision>
VirtualDimensionality<TPrecision>
::VirtualDimensionality()
: m_NumberOfPixels(0),
m_NumberOfEndmembers(0),
m_FAR(1.0E-3)
{
}
template<class TInputImage>
void
VirtualDimensionality<TInputImage>
::Compute()
{
// TODO check size
const unsigned int nbBands = m_Covariance.rows();
// Compute diagonalisation of sample covariance and correlation matrices
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();
m_NumberOfEndmembers = 0;
for(unsigned int i = 0; i < nbBands; ++i)
{
double sigma = vcl_sqrt( 2.0 / m_NumberOfPixels
* (eigenCovariance[i] + eigenCorrelation[i]
+ eigenCovariance[i] * eigenCorrelation[i]) );
boost::math::normal normalDist(0, sigma);
double tau = -boost::math::quantile(normalDist, m_FAR);
if (eigenCorrelation[i] - eigenCovariance[i] > tau )
m_NumberOfEndmembers++;
}
}
template <class TImage>
void
VirtualDimensionality<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;
}
} // end namespace otb
#endif
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