/usr/include/OTB-5.8/otbEigenvalueLikelihoodMaximisation.h is in libotb-dev 5.8.0+dfsg-3.
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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | /*=========================================================================
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 MERCHANT2ABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef otbEigenvalueLikelihoodMaximisation_h
#define otbEigenvalueLikelihoodMaximisation_h
#include "itkObjectFactory.h"
#include "itkLightObject.h"
#include "vnl/vnl_vector.h"
#include "vnl/vnl_matrix.h"
#include "vnl/algo/vnl_symmetric_eigensystem.h"
namespace otb
{
/** \class EigenvalueLikelihoodMaximisation
* \brief Estimates the number of endmembers in a hyperspectral image
*
* This filter applies the ELM (Eigenvalue Likelihood Maximisation) algorithm to a
* hyperspectral image and outputs the number of endmember.
* It takes as input the covariance and correlation matrices of the input data,
* the number of observed pixel for thoses matrices estimations,
* and outputs the number of endmembers, and the log-likelihood.
*
* References :
* "Unsupervised Endmember Extraction of Martian Hyperspectral Images",
* B.Luo, J. Chanussot, S. Dout\'e and X. Ceamanos,
* IEEE Whispers 2009, Grenoble France, 2009
*
* "Unsupervised classification of hyperspectral images by using linear unmixing algorithm",
* Luo, B. and Chanussot, J.,
* IEEE Int. Conf. On Image Processing(ICIP) 2009, Cairo, Egypte, 2009
*
* \ingroup Hyperspectral
*
* \ingroup OTBEndmembersExtraction
*/
template<class TPrecision>
class ITK_EXPORT EigenvalueLikelihoodMaximisation :
public itk::LightObject
{
public:
/** Standard Self typedef */
typedef EigenvalueLikelihoodMaximisation Self;
typedef itk::LightObject Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Runtime information support. */
itkTypeMacro(EigenvalueLikelihoodMaximisation, itk::LightObject);
/** Types to use for computations. */
typedef TPrecision PrecisionType;
typedef vnl_vector<PrecisionType> VectorType;
typedef vnl_matrix<PrecisionType> MatrixType;
void SetCovariance(const MatrixType& m)
{
m_Covariance = m;
}
void SetCorrelation(const MatrixType& m)
{
m_Correlation = m;
}
void SetNumberOfPixels(unsigned int n)
{
m_NumberOfPixels = n;
}
void Compute();
unsigned int GetNumberOfEndmembers()
{
return m_NumberOfEndmembers;
}
const VectorType& GetLikelihood()
{
return m_Likelihood;
}
protected:
EigenvalueLikelihoodMaximisation();
~EigenvalueLikelihoodMaximisation() ITK_OVERRIDE {}
void PrintSelf(std::ostream& os, itk::Indent indent) const ITK_OVERRIDE;
private:
EigenvalueLikelihoodMaximisation(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
MatrixType m_Covariance;
MatrixType m_Correlation;
unsigned int m_NumberOfPixels;
unsigned int m_NumberOfEndmembers;
VectorType m_Likelihood;
};
}
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbEigenvalueLikelihoodMaximisation.txx"
#endif
#endif
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