ClassifierEMStrategy.h

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/*  Copyright (C) 2008 National Institute For Space Research (INPE) - Brazil.
 
    This file is part of the TerraLib - a Framework for building GIS enabled applications.
 
    TerraLib is free software: you can redistribute it and/or modify
    it under the terms of the GNU Lesser General Public License as published by
    the Free Software Foundation, either version 3 of the License,
    or (at your option) any later version.
 
    TerraLib is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
    GNU Lesser General Public License for more details.
 
    You should have received a copy of the GNU Lesser General Public License
    along with TerraLib. See COPYING. If not, write to
    TerraLib Team at <terralib-team@terralib.org>.
 */
 
/*!
  \file terralib/rp/ClassifierEMStrategy.h
 
  \brief EM (Expectation-Maximization) strategy for pixel-based classification.
*/
 
#ifndef __TERRALIB_RP_INTERNAL_CLASSIFIEREMSTRATEGY_H
#define __TERRALIB_RP_INTERNAL_CLASSIFIEREMSTRATEGY_H
 
// TerraLib
#include "ClassifierStrategy.h"
#include "ClassifierStrategyFactory.h"
#include "Config.h"
 
// STL
#include <vector>
 
// Boost
#include <boost/numeric/ublas/matrix.hpp>
 
namespace te
{
  namespace rp
  {
    class AlgorithmParametersSerializer;
 
    /*!
      \class ClassifierEMStrategy
 
      \brief EM strategy for pixel-based classification. This is an unsupervised
      and pixel-based classification algorithm. Expectation-Maximization (EM) works
      iteratively by applying two steps: the E-step (Expectation) and the M-step
      (Maximization). The method aims to approximate the parameter estimates to
      real data distribution, along the iterations:
      1. The E-step calculates the conditional expectation of the complete a
      posteriori probability function.
      2. The M-step updates the parameter estimation.
      
      \ingroup rp_class
    */
    class TERPEXPORT ClassifierEMStrategy : public ClassifierStrategy
    {
      public:
 
        /*!
          \class Parameters
 
          \brief Classifier Parameters
        */
        class TERPEXPORT Parameters : public ClassifierStrategyParameters
        {
          public:
 
            unsigned int m_useRandomSamples;                    //!< If true, random samples will be used instead of regular spaced samples.
            unsigned int m_numberOfClusters;                    //!< The number of clusters (classes) to estimate in the image.
            unsigned int m_maxIterations;                       //!< The maximum of iterations (E/M steps) to perform if convergence is not achieved.
            unsigned int m_maxInputPoints;                      //!< The maximum number of points used to estimate the clusters (default = 1000).
            double m_epsilon;                                   //!< The stop criteria. When the clusters change in a value smaller then epsilon, the convergence is achieved.
            std::vector<std::vector<double> > m_clustersMeans;  //!< The previously estimated means of the clusters (optional).
 
            Parameters();
 
            ~Parameters();
 
            //overload
            const Parameters& operator=(const Parameters& params);
 
            //overload
            void reset() ;
 
            //overload
            AbstractParameters* clone() const;
 
            //overload
            bool serialize ( AlgorithmParametersSerializer& serializer ) const;
        };
 
      public:
 
        ClassifierEMStrategy();
 
        ~ClassifierEMStrategy();
 
        //overload
        bool initialize(ClassifierStrategyParameters const* const strategyParams) ;
 
        //overload
        bool execute() ;
 
      protected:
 
        bool m_isInitialized;                                        //!< True if this instance is initialized.
        ClassifierEMStrategy::Parameters m_parameters;               //!< Internal execution parameters.
 
    };
 
    /*!
      \class ClassifierEMStrategyFactory
 
      \brief Raster EM Classifier strategy factory.
    */
    class TERPEXPORT ClassifierEMStrategyFactory : public ClassifierStrategyFactory
    {
      public:
 
        ClassifierEMStrategyFactory();
 
        ~ClassifierEMStrategyFactory();
 
        //overload
        te::rp::ClassifierStrategy* build();
    };
 
  } // end namespace rp
}   // end namespace te
 
#endif // __TERRALIB_RP_INTERNAL_CLASSIFIEREMSTRATEGY_H