20#ifndef __TERRALIB_RP_RADAR_INTERNAL_MULTISOURCECLASSIFIER_OUTPUT_PARAMETERS_H
21#define __TERRALIB_RP_RADAR_INTERNAL_MULTISOURCECLASSIFIER_OUTPUT_PARAMETERS_H
26#include "../../raster/Raster.h"
27#include "../AlgorithmOutputParameters.h"
30#include <boost/numeric/ublas/matrix.hpp>
Raster Processing algorithm output parameters base interface.
MultiSourceClassifier output parameters.
const MultiSourceClassifierOutputParameters & operator=(const MultiSourceClassifierOutputParameters ¶ms)
std::unique_ptr< te::rst::Raster > newRaster
The generated output raster for classified iamge.
std::unique_ptr< te::rst::Raster > m_outputRasterMapPtr
The generated output raster for reliability map (statistical test).
std::vector< double > m_valuesP
P-values of single source classification result.
std::vector< std::vector< double > > m_outAllDistances
std::map< std::string, std::string > m_rInfoStatisticPerClass
The necessary information to create the output statistic per class file.
double m_overallAccuracy
The overall accuracy.
std::vector< double > m_valuesS
Statiscal test values of classification result.
unsigned int m_outputRasterBand
std::vector< std::vector< double > > m_outDistancesPerClass
Stochastic distance per class values of classification result.
std::unique_ptr< te::rst::Raster > m_outputRasterClassImageFuzzyPtr
The generated output raster for classified iamge for Fuzzy.
std::map< std::string, std::string > m_rInfoMap
The necessary information to create the output raster for reliability map (statistical test).
MultiSourceClassifierOutputParameters()
std::map< std::string, std::string > m_rInfoClassImageFuzzy
The necessary information to create the output raster for classified image for Fuzzy.
std::vector< double > m_pValuesMultiplication
P-values of multiplication combination multi-source classification result.
std::map< std::string, std::string > m_outinfo
The necessary information to create the output.
std::vector< double > m_pValuesMinimum
P-values of minimum combination multi-source classification result.
std::vector< std::vector< unsigned int > > m_confusionMatrix
The confusion matrix.
MultiSourceClassifierOutputParameters(const MultiSourceClassifierOutputParameters &)
void reset()
Clear all internal allocated resources and reset the parameters instance to its initial state.
std::vector< std::vector< double > > m_outAllStatistics
double m_varianceOfKappa
The variance of Kappa.
std::unique_ptr< te::rst::Raster > m_outputRasterClassImagePtr
The generated output raster for classified iamge.
std::string m_rType
Output raster data source type (as described in te::raster::RasterFactory).
std::vector< double > m_distances
Stochastic distance values of classification result.
std::map< std::string, std::string > m_rInfoPValuePerClass
The necessary information to create the output p-value per class file.
~MultiSourceClassifierOutputParameters()
std::map< std::string, std::string > m_rInfoMapPValue
The necessary information to create the output raster for reliability map (p-value).
std::vector< double > m_pValuesSum
P-values of sum combination multi-source classification result.
std::vector< std::vector< double > > m_outPValuesPerClass
P-values per class values of classification result.
std::vector< std::vector< double > > m_bandsFuzzy
The bands values in Fuzzy.
std::map< std::string, std::string > m_rInfoClassImage
The necessary information to create the output raster for classified image.
std::vector< double > m_IDSegment
ID of segments.
std::vector< std::vector< double > > m_outStatisticsPerClass
Statistics per class values of classification result.
AbstractParameters * clone() const
Create a clone copy of this instance.
std::unique_ptr< te::rst::Raster > m_outputRasterMapPValuePtr
The generated output raster for reliability map (p-value).
std::map< std::string, std::string > m_rInfoDistancePerClass
The necessary information to create the output stochastic distance per class file.
double m_kappaCoefficient
The Kappa coefficient.
#define TERPEXPORT
You can use this macro in order to export/import classes and functions from this module.