svn-gvsig-desktop / trunk / extensions / extRemoteSensing / src / org / gvsig / remotesensing / classification / ClassificationMinimumDistanceProcess.java @ 16598
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/* gvSIG. Sistema de Informaci?n Geogr?fica de la Generalitat Valenciana
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*
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* Copyright (C) 2006 Instituto de Desarrollo Regional and Generalitat Valenciana.
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*
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* This program is free software; you can redistribute it and/or
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* modify it under the terms of the GNU General Public License
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* as published by the Free Software Foundation; either version 2
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* of the License, or (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software
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* Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307,USA.
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*
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* For more information, contact:
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*
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* Generalitat Valenciana
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* Conselleria d'Infraestructures i Transport
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* Av. Blasco Iba?ez, 50
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* 46010 VALENCIA
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* SPAIN
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*
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* +34 963862235
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* gvsig@gva.es
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* www.gvsig.gva.es
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*
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* or
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*
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* Instituto de Desarrollo Regional (Universidad de Castilla La-Mancha)
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* Campus Universitario s/n
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* 02071 Alabacete
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* Spain
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*
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* +34 967 599 200
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*/
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package org.gvsig.remotesensing.classification; |
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import java.util.ArrayList; |
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import org.gvsig.raster.buffer.RasterBuffer; |
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import org.gvsig.raster.dataset.IBuffer; |
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import org.gvsig.raster.dataset.IRasterDataSource; |
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import org.gvsig.raster.grid.GridException; |
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import org.gvsig.raster.grid.roi.ROI; |
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import org.gvsig.raster.process.RasterTaskQueue; |
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import org.gvsig.raster.util.RasterToolsUtil; |
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import com.iver.andami.PluginServices; |
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import com.iver.cit.gvsig.project.documents.view.gui.View; |
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/** ClassificationMinimumDistanceProcess implementa el m?todo de clasificaci?n de
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* m?nima distancia.
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*
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* @see ClassificationGeneralProcess
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* @author Alejandro Mu?oz Sanchez (alejandro.munoz@uclm.es)
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* @author Diego Guerrero Sevilla (diego.guerrero@uclm.es)
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* @version 19/10/2007
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*/
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public class ClassificationMinimumDistanceProcess extends ClassificationGeneralProcess { |
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private double means[][] = null; |
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private int bandCount = 0; |
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/**
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* Class constructor.
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* @param raster raster a clasificar
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* @param rois lista de rois
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* @param bandList bandas habilitadas
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* @param view vista
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* @param fileNameOutput path de fichero de salida
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*/
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public ClassificationMinimumDistanceProcess(IRasterDataSource raster, ArrayList rois, int bandList[], View view, String fileNameOutput){ |
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this.raster= raster;
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this.rois = rois;
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this.view=view;
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this.fileNameOutput= fileNameOutput;
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this.bandList = bandList;
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numClases = rois.size(); |
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} |
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/**
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* Lanzar el Thread del proceso de clasificaci?n por el m?todo de m?nima distancia.
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*/
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public void start() { |
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blinker = new Thread(this); |
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blinker.start(); |
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} |
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/*
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* (non-Javadoc)
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* @see java.lang.Runnable#run()
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*/
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public void run() { |
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RasterTaskQueue.register(rasterTask); |
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setGrid(); |
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rasterResult= RasterBuffer.getBuffer(IBuffer.TYPE_DOUBLE, inputGrid.getRasterBuf().getWidth(), inputGrid.getRasterBuf().getHeight(), 1, true); |
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int c=0; |
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int iNY= inputGrid.getLayerNY();
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int iNX= inputGrid.getLayerNX();
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bandCount = inputGrid.getBandCount(); |
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try{
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means = new double[numClases][bandCount]; |
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for (int clase=0; clase<numClases; clase++) |
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for (int i=0;i<bandCount;i++){ |
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((ROI)rois.get(clase)).setBandToOperate(bandList[i]); |
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means[clase][i]=((ROI)rois.get(clase)).getMeanValue(); |
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} |
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int inputGridNX = inputGrid.getNX();
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int datType = inputGrid.getRasterBuf().getDataType();
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// Caso Buffer tipo Byte
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if (datType == RasterBuffer.TYPE_BYTE){
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byte data[]= new byte[bandCount]; |
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for(int i=0; i<iNY;i++){ |
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for(int j=0; j<iNX;j++){ |
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inputGrid.getRasterBuf().getElemByte(i, j, data); |
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c= getPixelClassForTypeByte(data); |
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rasterResult.setElem(i, j, 0,(double) c); |
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} |
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percent = i*100/inputGridNX;
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} |
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} |
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// Caso Buffer tipo Short
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else if (datType == RasterBuffer.TYPE_SHORT){ |
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short data[]= new short[bandCount]; |
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for(int i=0; i<iNY;i++){ |
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for(int j=0; j<iNX;j++){ |
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inputGrid.getRasterBuf().getElemShort(i, j, data); |
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c= getPixelClassForTypeShort(data); |
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rasterResult.setElem(i, j, 0,(double)c); |
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} |
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percent = i*100/inputGridNX;
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} |
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} |
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// Caso Buffer tipo Int
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else if (datType == RasterBuffer.TYPE_INT){ |
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int data[]= new int[bandCount]; |
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for(int i=0; i<iNY;i++){ |
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for(int j=0; j<iNX;j++){ |
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inputGrid.getRasterBuf().getElemInt(i, j, data); |
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c= getPixelClassForTypeInt(data); |
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rasterResult.setElem(i, j, 0,(double) c); |
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} |
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percent = i*100/inputGridNX;
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} |
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} |
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// Caso Buffer tipo Float
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else if (datType == RasterBuffer.TYPE_FLOAT){ |
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float data[]= new float[bandCount]; |
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for(int i=0; i<iNY;i++){ |
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for(int j=0; j<iNX;j++){ |
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inputGrid.getRasterBuf().getElemFloat(i, j, data); |
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c= getPixelClassForTypeFloat(data); |
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rasterResult.setElem(i, j, 0,(double) c); |
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} |
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percent = i*100/inputGridNX;
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} |
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} |
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// Caso Buffer tipo Float
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else if (datType == RasterBuffer.TYPE_DOUBLE){ |
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double data[]= new double[bandCount]; |
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for(int i=0; i<iNY;i++){ |
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for(int j=0; j<iNX;j++){ |
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inputGrid.getRasterBuf().getElemDouble(i, j, data); |
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c= getPixelClassForTypeDouble(data); |
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rasterResult.setElem(i, j, 0,(double) c); |
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} |
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percent = i*100/inputGridNX;
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} |
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} |
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writeToFile(); |
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}catch (GridException e) {
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RasterToolsUtil.messageBoxError(PluginServices.getText(this, "grid_error"), this, e); |
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}finally {
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RasterTaskQueue.remove(rasterTask); |
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} |
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} |
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/**
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* M?todo que implementa el clasificador de m?nima distancia.
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* Para cada pixel, obtiene la clase para la que la distancia eucl?dea respecto al
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* vector de medias es m?nima
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*
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* @param array de tipo byte con los valores del pixel en cada una de las bandas
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* @return clase a la que pertenece el pixel
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*/
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public int getPixelClassForTypeByte(byte[] pixelBandsValues) { |
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int clasefinal=0; double distanciaFinal=0.0; |
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for (int clase=0; clase<numClases; clase++) |
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{
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double[] y = new double[bandCount]; |
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double distancia=0; |
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for (int i=0;i<bandCount;i++){ |
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y[i]=(pixelBandsValues[i]-means[clase][i]) * (pixelBandsValues[i]-means[clase][i]); |
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//y[i]=Math.pow((pixelBandsValues[i]-means[clase][i]),2);
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distancia+=y[i]; |
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} |
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//distancia= Math.sqrt(distancia); //No hacer la raiz: Para comparar da igual la distancia que la distancia al cuadrado...
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if (clase==0) |
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distanciaFinal=distancia; |
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else if (distancia<distanciaFinal){ |
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distanciaFinal = distancia; |
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clasefinal = clase; |
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} |
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} |
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return clasefinal;
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} |
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/**
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* M?todo que implementa el clasificador de m?nima distancia.
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* Para cada pixel, obtiene la clase para la que la distancia eucl?dea respecto al
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* vector de medias es m?nima
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*
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* @param array de tipo short con los valores del pixel en cada una de las bandas
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* @return clase a la que pertenece el pixel
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*/
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public int getPixelClassForTypeShort(short[] pixelBandsValues) { |
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int clasefinal=0; double distanciaFinal=0.0; |
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for (int clase=0; clase<numClases; clase++) |
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{
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double[] y = new double[bandCount]; |
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double distancia=0; |
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for (int i=0;i<bandCount;i++){ |
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y[i]=(pixelBandsValues[i]-means[clase][i]) * (pixelBandsValues[i]-means[clase][i]); |
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//y[i]=Math.pow((pixelBandsValues[i]-means[clase][i]),2);
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distancia+=y[i]; |
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} |
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//distancia= Math.sqrt(distancia); //No hacer la raiz: Para comparar da igual la distancia que la distancia al cuadrado...
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if (clase==0) |
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distanciaFinal=distancia; |
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else if (distancia<distanciaFinal){ |
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distanciaFinal= distancia; |
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clasefinal=clase; |
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} |
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} |
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return clasefinal;
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} |
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/**
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* M?todo que implementa el clasificador de m?nima distancia.
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* Para cada pixel, obtiene la clase para la que la distancia eucl?dea respecto al
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* vector de medias es m?nima
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*
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* @param array de tipo int con los valores del pixel en cada una de las bandas
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* @return clase a la que pertenece el pixel
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*/
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public int getPixelClassForTypeInt(int[] pixelBandsValues) { |
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int clasefinal=0; double distanciaFinal=0.0; |
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for (int clase=0; clase<numClases; clase++) |
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{
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double[] y = new double[bandCount]; |
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double distancia=0; |
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for (int i=0;i<bandCount;i++){ |
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y[i]=(pixelBandsValues[i]-means[clase][i]) * (pixelBandsValues[i]-means[clase][i]); |
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//y[i]=Math.pow((pixelBandsValues[i]-means[clase][i]),2);
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distancia+=y[i]; |
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} |
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//distancia= Math.sqrt(distancia); //No hacer la raiz: Para comparar da igual la distancia que la distancia al cuadrado...
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if (clase==0) |
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distanciaFinal=distancia; |
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else if (distancia<distanciaFinal){ |
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distanciaFinal= distancia; |
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clasefinal=clase; |
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} |
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} |
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return clasefinal;
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} |
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/**
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* M?todo que implementa el clasificador de m?nima distancia.
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* Para cada pixel, obtiene la clase para la que la distancia eucl?dea respecto al
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* vector de medias es m?nima
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*
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* @param array de tipo float con los valores del pixel en cada una de las bandas
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* @return clase a la que pertenece el pixel
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*/
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public int getPixelClassForTypeFloat(float[] pixelBandsValues) { |
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int clasefinal=0; double distanciaFinal=0.0; |
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for (int clase=0; clase<numClases; clase++) |
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{
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double[] y = new double[bandCount]; |
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double distancia=0; |
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for (int i=0;i<bandCount;i++){ |
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y[i]=(pixelBandsValues[i]-means[clase][i]) * (pixelBandsValues[i]-means[clase][i]); |
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//y[i]=Math.pow((pixelBandsValues[i]-means[clase][i]),2);
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distancia+=y[i]; |
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} |
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//distancia= Math.sqrt(distancia); //No hacer la raiz: Para comparar da igual la distancia que la distancia al cuadrado...
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if (clase==0) |
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distanciaFinal=distancia; |
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else if (distancia<distanciaFinal){ |
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distanciaFinal= distancia; |
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clasefinal=clase; |
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} |
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} |
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return clasefinal;
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} |
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/**
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* M?todo que implementa el clasificador de m?nima distancia.
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* Para cada pixel, obtiene la clase para la que la distancia eucl?dea respecto al
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* vector de medias es m?nima
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*
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* @param array de tipo double con los valores del pixel en cada una de las bandas
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* @return clase a la que pertenece el pixel
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*/
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public int getPixelClassForTypeDouble(double[] pixelBandsValues) { |
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int clasefinal=0; double distanciaFinal=0.0; |
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for (int clase=0; clase<numClases; clase++) |
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{
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double[] y = new double[bandCount]; |
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double distancia=0; |
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for (int i=0;i<bandCount;i++){ |
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y[i]=(pixelBandsValues[i]-means[clase][i]) * (pixelBandsValues[i]-means[clase][i]); |
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//y[i]=Math.pow((pixelBandsValues[i]-means[clase][i]),2);
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distancia+=y[i]; |
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} |
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//distancia= Math.sqrt(distancia); //No hacer la raiz: Para comparar da igual la distancia que la distancia al cuadrado...
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if (clase==0) |
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distanciaFinal=distancia; |
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else if (distancia<distanciaFinal){ |
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distanciaFinal= distancia; |
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clasefinal=clase; |
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} |
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} |
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return clasefinal;
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} |
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} |