//--------------------------------------------------------- bool CGrid_Normalise::On_Execute(void) { CSG_Grid *pGrid = Parameters("INPUT")->asGrid(); if( pGrid->Get_StdDev() <= 0.0 ) { return( false ); } if( pGrid != Parameters("OUTPUT")->asGrid() ) { pGrid = Parameters("OUTPUT")->asGrid(); pGrid ->Assign(Parameters("INPUT")->asGrid()); } pGrid->Fmt_Name("%s (%s)", pGrid->Get_Name(), _TL("Normalized")); //----------------------------------------------------- double Minimum, Maximum, Offset, Scale; Minimum = Parameters("RANGE")->asRange()->Get_Min(); Maximum = Parameters("RANGE")->asRange()->Get_Max(); Offset = pGrid->Get_Min(); Scale = (Maximum - Minimum) / pGrid->Get_Range(); for(int y=0; y<Get_NY() && SG_UI_Process_Set_Progress(y, Get_NY()); y++) { for(int x=0; x<Get_NX(); x++) { if( !pGrid->is_NoData(x, y) ) { pGrid->Set_Value(x, y, Minimum + Scale * (pGrid->asDouble(x, y) - Offset)); } } } //----------------------------------------------------- if( pGrid == Parameters("INPUT")->asGrid() ) { DataObject_Update(pGrid); } return( true ); }
//--------------------------------------------------------- bool CPanSharp_PCA::On_Execute(void) { //----------------------------------------------------- bool bResult; CSG_Parameters Tool_Parms; CSG_Table Eigen; //----------------------------------------------------- // get the principal components for the low resolution bands SG_RUN_TOOL_KEEP_PARMS(bResult, "statistics_grid", 8, Tool_Parms, SG_TOOL_PARAMETER_SET("GRIDS" , Parameters("GRIDS" )) && SG_TOOL_PARAMETER_SET("METHOD" , Parameters("METHOD")) && SG_TOOL_PARAMETER_SET("EIGEN" , &Eigen) && SG_TOOL_PARAMETER_SET("COMPONENTS", 0) // get all components ); if( !bResult ) { return( false ); } //----------------------------------------------------- CSG_Parameter_Grid_List *pPCA = Tool_Parms.Get_Parameter("PCA")->asGridList(); int i, n = pPCA->Get_Grid_Count(); CSG_Grid *PCA = new CSG_Grid[n]; CSG_Grid *pPan = Parameters("PAN")->asGrid(); //----------------------------------------------------- // replace first principal component with the high resolution panchromatic band Process_Set_Text(_TL("Replace first PC with PAN")); double Offset_Pan, Offset, Scale; if( Parameters("PAN_MATCH")->asInt() == 0 ) // scale PAN band to fit first PC histogram { Offset_Pan = pPan->Get_Min(); Offset = pPCA->Get_Grid(0)->Get_Min(); Scale = pPCA->Get_Grid(0)->Get_Range() / pPan->Get_Range(); } else { Offset_Pan = pPan->Get_Mean(); Offset = pPCA->Get_Grid(0)->Get_Mean(); Scale = pPCA->Get_Grid(0)->Get_StdDev() / pPan->Get_StdDev(); } PCA[0].Create(Get_System()); for(int y=0; y<Get_NY() && Set_Progress(y); y++) { #pragma omp parallel for for(int x=0; x<Get_NX(); x++) { if( pPan->is_NoData(x, y) ) { PCA[0].Set_NoData(x, y); } else { PCA[0].Set_Value(x, y, Offset + Scale * (pPan->asDouble(x, y) - Offset_Pan)); } } } //----------------------------------------------------- // resample all other PCs to match the high resolution of the PAN band TSG_Grid_Resampling Resampling = Get_Resampling(Parameters("RESAMPLING")->asInt()); for(i=1; i<n; i++) { Process_Set_Text("%s: %s ...", _TL("Resampling"), pPCA->Get_Grid(i)->Get_Name()); PCA[i].Create(Get_System()); PCA[i].Assign(pPCA->Get_Grid(i), Resampling); delete(pPCA->Get_Grid(i)); // PCA tool was unmanaged, so we have to delete the output } delete(pPCA->Get_Grid(0)); pPCA->Del_Items(); for(i=0; i<n; i++) { pPCA->Add_Item(&PCA[i]); } //----------------------------------------------------- // inverse principal component rotation for the high resolution bands SG_RUN_TOOL_KEEP_PARMS(bResult, "statistics_grid", 10, Tool_Parms, SG_TOOL_PARAMETER_SET("PCA" , Tool_Parms("PCA")) && SG_TOOL_PARAMETER_SET("GRIDS", Parameters("SHARPEN")) && SG_TOOL_PARAMETER_SET("EIGEN", &Eigen) ); delete[](PCA); if( !bResult ) { return( false ); } CSG_Parameter_Grid_List *pHiRes = Parameters("SHARPEN")->asGridList(); CSG_Parameter_Grid_List *pLoRes = Parameters("GRIDS" )->asGridList(); CSG_Parameter_Grid_List *pGrids = Tool_Parms("GRIDS" )->asGridList(); if( !Parameters("OVERWRITE")->asBool() ) { pHiRes->Del_Items(); } for(i=0; i<pLoRes->Get_Grid_Count() && i<pGrids->Get_Grid_Count(); i++) { if( pHiRes->Get_Grid(i) ) { pHiRes->Get_Grid(i)->Assign(pGrids->Get_Grid(i)); delete(pGrids->Get_Grid(i)); } else { pHiRes->Add_Item(pGrids->Get_Grid(i)); } pHiRes->Get_Grid(i)->Set_Name(pLoRes->Get_Grid(i)->Get_Name()); } return( true ); }
//--------------------------------------------------------- bool CPanSharp_IHS::On_Execute(void) { //----------------------------------------------------- TSG_Grid_Resampling Resampling = Get_Resampling(Parameters("RESAMPLING")->asInt()); //----------------------------------------------------- int y; CSG_Grid *pPan = Parameters("PAN")->asGrid(); //----------------------------------------------------- Process_Set_Text("%s: %s ...", _TL("Resampling"), Parameters("R")->asGrid()->Get_Name()); CSG_Grid *pR = Parameters("R_SHARP")->asGrid(); pR->Assign (Parameters("R")->asGrid(), Resampling); pR->Set_Name(Parameters("R")->asGrid()->Get_Name()); Process_Set_Text("%s: %s ...", _TL("Resampling"), Parameters("G")->asGrid()->Get_Name()); CSG_Grid *pG = Parameters("G_SHARP")->asGrid(); pG->Assign (Parameters("G")->asGrid(), Resampling); pG->Set_Name(Parameters("G")->asGrid()->Get_Name()); Process_Set_Text("%s: %s ...", _TL("Resampling"), Parameters("B")->asGrid()->Get_Name()); CSG_Grid *pB = Parameters("B_SHARP")->asGrid(); pB->Assign (Parameters("B")->asGrid(), Resampling); pB->Set_Name(Parameters("B")->asGrid()->Get_Name()); //----------------------------------------------------- Process_Set_Text(_TL("RGB to IHS")); double rMin = pR->Get_Min(), rRange = pR->Get_Range(); double gMin = pG->Get_Min(), gRange = pG->Get_Range(); double bMin = pB->Get_Min(), bRange = pB->Get_Range(); for(y=0; y<pPan->Get_NY() && Set_Progress(y, pPan->Get_NY()); y++) { #pragma omp parallel for for(int x=0; x<pPan->Get_NX(); x++) { bool bNoData = true; if( pPan->is_NoData(x, y) || pR->is_NoData(x, y) || pG->is_NoData(x, y) || pB->is_NoData(x, y) ) { pR->Set_NoData(x, y); pG->Set_NoData(x, y); pB->Set_NoData(x, y); } else { double r = (pR->asDouble(x, y) - rMin) / rRange; if( r < 0.0 ) r = 0.0; else if( r > 1.0 ) r = 1.0; double g = (pG->asDouble(x, y) - gMin) / gRange; if( g < 0.0 ) g = 0.0; else if( g > 1.0 ) g = 1.0; double b = (pB->asDouble(x, y) - bMin) / bRange; if( b < 0.0 ) b = 0.0; else if( b > 1.0 ) b = 1.0; double h, s, i = r + g + b; if( i <= 0.0 ) { h = 0.0; s = 0.0; } else { if( r == g && g == b ) { h = 0.0; } else if( b < r && b < g ) { h = (g - b) / (i - 3 * b) ; } else if( r < g && r < b ) { h = (b - r) / (i - 3 * r) + 1; } else { h = (r - g) / (i - 3 * g) + 2; } if ( 0.0 <= h && h < 1.0 ) { s = (i - 3 * b) / i; } else if( 1.0 <= h && h < 2.0 ) { s = (i - 3 * r) / i; } else { s = (i - 3 * g) / i; } } pR->Set_Value(x, y, i); pG->Set_Value(x, y, s); pB->Set_Value(x, y, h); } } } //----------------------------------------------------- double Offset_Pan, Offset, Scale; if( Parameters("PAN_MATCH")->asInt() == 0 ) { Offset_Pan = pPan->Get_Min(); Offset = pR->Get_Min(); Scale = pR->Get_Range() / pPan->Get_Range(); } else { Offset_Pan = pPan->Get_Mean(); Offset = pR->Get_Mean(); Scale = pR->Get_StdDev() / pPan->Get_StdDev(); } //----------------------------------------------------- Process_Set_Text(_TL("IHS to RGB")); for(y=0; y<pPan->Get_NY() && Set_Progress(y, pPan->Get_NY()); y++) { #pragma omp parallel for for(int x=0; x<pPan->Get_NX(); x++) { if( !pR->is_NoData(x, y) ) { double i = Offset + Scale * (pPan->asDouble(x, y) - Offset_Pan); double s = pG ->asDouble(x, y); double h = pB ->asDouble(x, y); double r, g, b; if ( 0.0 <= h && h < 1.0 ) { r = i * (1 + 2 * s - 3 * s * h) / 3; g = i * (1 - s + 3 * s * h) / 3; b = i * (1 - s ) / 3; } else if( 1.0 <= h && h < 2.0 ) { r = i * (1 - s ) / 3; g = i * (1 + 2 * s - 3 * s * (h - 1)) / 3; b = i * (1 - s + 3 * s * (h - 1)) / 3; } else { r = i * (1 - s + 3 * s * (h - 2)) / 3; g = i * (1 - s ) / 3; b = i * (1 + 2 * s - 3 * s * (h - 2)) / 3; } pR->Set_Value(x, y, rMin + r * rRange); pG->Set_Value(x, y, gMin + g * gRange); pB->Set_Value(x, y, bMin + b * bRange); } } } //----------------------------------------------------- return( true ); }
//--------------------------------------------------------- CSG_String CPresence_Prediction::Get_Feature(int x, int y, int i) { CSG_Grid *pFeature = m_Features[i].pGrid; if( m_nNumClasses > 1 && m_Features[i].bNumeric ) { return( CSG_String::Format("%d", (int)(m_nNumClasses * (pFeature->asDouble(x, y) - pFeature->Get_Min()) / pFeature->Get_Range())) ); } return( SG_Get_String(pFeature->asDouble(x, y), -2) ); }
//--------------------------------------------------------- bool CPresence_Prediction::On_Execute(void) { //----------------------------------------------------- EventSet DL_Events ; m_DL_Events = &DL_Events ; GISTrainer DL_Trainer; m_DL_Trainer = &DL_Trainer; MaxEntModel DL_Model ; m_DL_Model = &DL_Model ; m_YT_Model.clear(); //----------------------------------------------------- CSG_Grid *pPrediction = Parameters("PREDICTION" )->asGrid(); CSG_Grid *pProbability = Parameters("PROBABILITY")->asGrid(); if( !pPrediction ->Get_Range() ) DataObject_Set_Colors(pPrediction , 11, SG_COLORS_YELLOW_GREEN); if( !pProbability->Get_Range() ) DataObject_Set_Colors(pProbability, 11, SG_COLORS_YELLOW_GREEN); m_Method = Parameters("METHOD" )->asInt (); m_nNumClasses = Parameters("NUM_CLASSES" )->asInt (); m_bYT_Weights = Parameters("YT_NUMASREAL")->asBool(); //----------------------------------------------------- CSG_Array Features; if( !Get_Features(Features) ) { Error_Set(_TL("invalid features")); return( false ); } //----------------------------------------------------- if( m_Method == 0 && SG_File_Exists(Parameters("YT_FILE_LOAD")->asString()) ) { if( !Get_File(Parameters("YT_FILE_LOAD")->asString()) ) { return( false ); } } else if( !Get_Training() ) { return( false ); } //----------------------------------------------------- Process_Set_Text(_TL("prediction")); for(int y=0; y<Get_NY() && Set_Progress(y); y++) { #pragma omp parallel for for(int x=0; x<Get_NX(); x++) { int i; CSG_Strings Values; for(i=0; i<m_nFeatures; i++) { if( !m_Features[i].pGrid->is_NoData(x, y) ) { Values.Add(Get_Feature(x, y, i)); } else { break; } } if( Values.Get_Count() != m_nFeatures ) { pPrediction ->Set_NoData(x, y); pProbability->Set_NoData(x, y); } else switch( m_Method ) { //--------------------------------------------- default: // Kyoshida { ME_Sample Sample; for(i=0; i<m_nFeatures; i++) { if( m_bYT_Weights && m_Features[i].bNumeric ) { Sample.add_feature(m_Features[i].Name, m_Features[i].pGrid->asDouble(x, y)); } else { Sample.add_feature(Values[i].b_str()); } } vector<double> Probs = m_YT_Model.classify(Sample); pPrediction ->Set_Value(x, y, m_YT_Model.get_class_id(Sample.label) == 0 ? 1 : 0); pProbability->Set_Value(x, y, Probs[0]); } break; //--------------------------------------------- case 1: // Dekang Lin { MaxEntEvent Event; Event.count(1); for(i=0; i<m_nFeatures; i++) { Event.push_back(m_DL_Trainer->getId(Values[i].b_str())); } vector<double> Probs; pPrediction ->Set_Value(x, y, m_DL_Model->getProbs(Event, Probs) == 0 ? 1 : 0); pProbability->Set_Value(x, y, Probs[0]); } break; } } } return( true ); }