svn-gvsig-desktop / trunk / extensions / extRemoteSensing / src / org / gvsig / remotesensing / classification / ClassificationParallelepipedProcess.java @ 18829
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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.fmap.raster.layers.FLyrRasterSE; |
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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.grid.GridException; |
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import org.gvsig.raster.grid.roi.ROI; |
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import com.iver.cit.gvsig.project.documents.view.gui.View; |
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/** ClassificationParallelepipedProcess implementa el m?todo de clasificaci?n de
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* paralelepipedos o hipercubos.
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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 ClassificationParallelepipedProcess extends ClassificationGeneralProcess{ |
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private int bandCount = 0; |
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double varianza[][] = null; |
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double medias[][] = null; |
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int defaultClass =0; |
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double stevcoef =0; |
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/**
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* M?todo que implementa el clasificador por paralelepipedos.
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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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for (int clase=0; clase<numClases; clase++) |
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{
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boolean inClass= true; |
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for(int i=0; i< bandCount; i++) |
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{
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if(!((medias[clase][i]-stevcoef*Math.sqrt(varianza[clase][i])<pixelBandsValues[i]) && |
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(pixelBandsValues[i] <medias[clase][i]+stevcoef*Math.sqrt(varianza[clase][i]))))
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{
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inClass= false;
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} |
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} |
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if(inClass==true) |
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return clase;
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} |
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return defaultClass;
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} // Fin del metodo
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/**
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* M?todo que implementa el clasificador por paralelepipedos.
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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 primera clase a la que pertenece el pixel.
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*/
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public int getPixelClassForTypeShort(short[] pixelBandsValues) { |
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for (int clase=0; clase<numClases; clase++) |
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{
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boolean inClass= true; |
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for(int i=0; i< bandCount; i++) |
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{
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if(!((medias[clase][i]-stevcoef*Math.sqrt(varianza[clase][i])<pixelBandsValues[i]) && |
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(pixelBandsValues[i] <medias[clase][i]+stevcoef*Math.sqrt(varianza[clase][i]))))
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{
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inClass= false;
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} |
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} |
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if(inClass==true) |
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return clase;
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} |
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return defaultClass;
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} |
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/**
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* M?todo que implementa el clasificador por paralelepipedos.
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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 primera clase a la que pertenece el pixel.
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*/
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public int getPixelClassForTypeInt(int[] pixelBandsValues) { |
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for (int clase=0; clase<numClases; clase++) |
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{
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boolean inClass= true; |
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for(int i=0; i< bandCount; i++) |
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{
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if(!((medias[clase][i]-stevcoef*Math.sqrt(varianza[clase][i])<pixelBandsValues[i]) && |
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(pixelBandsValues[i] <medias[clase][i]+stevcoef*Math.sqrt(varianza[clase][i]))))
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{
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inClass= false;
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} |
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} |
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if(inClass==true) |
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return clase;
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} |
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return defaultClass;
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} |
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/**
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* M?todo que implementa el clasificador por paralelepipedos.
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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 primera clase a la que pertenece el pixel.
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*/
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public int getPixelClassForTypeFloat(float[] pixelBandsValues) { |
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for (int clase=0; clase<numClases; clase++) |
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{
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boolean inClass= true; |
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for(int i=0; i< bandCount; i++) |
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{
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if(!((medias[clase][i]-stevcoef*Math.sqrt(varianza[clase][i])<pixelBandsValues[i]) && |
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(pixelBandsValues[i] <medias[clase][i]+stevcoef*Math.sqrt(varianza[clase][i]))))
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{
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inClass= false;
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} |
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} |
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if(inClass==true) |
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return clase;
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} |
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return defaultClass;
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} |
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/**
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* M?todo que implementa el clasificador por paralelepipedos.
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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 primera clase a la que pertenece el pixel.
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*/
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public int getPixelClassForTypeDouble(double[] pixelBandsValues) { |
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for (int clase=0; clase<numClases; clase++) |
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{
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boolean inClass= true; |
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for(int i=0; i< bandCount; i++) |
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{
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if(!((medias[clase][i]-stevcoef*Math.sqrt(varianza[clase][i])<pixelBandsValues[i]) && |
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(pixelBandsValues[i] <medias[clase][i]+stevcoef*Math.sqrt(varianza[clase][i]))))
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{
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inClass= false;
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} |
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} |
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if(inClass==true) |
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return clase;
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} |
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return defaultClass;
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} |
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/** Metodo que recoge los parametros del proceso de clasificacion de
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* por paralelepipedos
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* <LI>rasterSE: Capa de entrada para la clasificaci?n</LI>
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* <LI> rois: lista de rois</LI>
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* <LI> bandList:bandas habilitadas </LI>
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* <LI>view: vista sobre la que se carga la capa al acabar el proceso</LI>
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* <LI>filename: path con el fichero de salida</LI>
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* <LI>stevcoef: coeficiente que establece el de desviacion maxima</LI>
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*/
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public void init() { |
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rasterSE= (FLyrRasterSE)getParam("layer");
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rois = (ArrayList)getParam("rois"); |
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view=(View)getParam("view"); |
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filename= getStringParam("filename");
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bandList = (int[])getParam("bandList"); |
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stevcoef= ((Double)getParam("dev")).doubleValue(); |
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bandCount = bandList.length; |
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numClases = rois.size(); |
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defaultClass= numClases; |
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medias= new double[numClases][bandCount]; |
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varianza= new double[numClases][bandCount]; |
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// Calculo de estadisticas de clases
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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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try {
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medias[clase][i]=((ROI)rois.get(clase)).getMeanValue(); |
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varianza[clase][i]=((ROI)rois.get(clase)).getVariance(); |
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} catch (GridException e) {
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e.printStackTrace(); |
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} |
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} |
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} |
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/** Proceso de clasificacion por paralelepipedos*/
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public void process() throws InterruptedException { |
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setGrid(); |
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withdefaultClass=true;
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rasterResult= RasterBuffer.getBuffer(IBuffer.TYPE_BYTE, inputGrid.getRasterBuf().getWidth(), |
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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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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,(byte) 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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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,(byte) 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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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,(byte) 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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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,(byte) 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 double
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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,(byte) 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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} |
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} |