private void doParalellpiped() {
int iNX, iNY;
int x, y;
int iMatchingClass = 0;
int iClass, iGrid;
final double dMean[][] = new double[m_Classes.size()][m_Window.length];
final double dStdDev[][] = new double[m_Classes.size()][m_Window.length];
double dValue;
ArrayList stats;
MeanAndStdDev substats;
Set set;
Iterator iter;
iNX = m_Output.getWindowGridExtent().getNX();
iNY = m_Output.getWindowGridExtent().getNY();
set = m_Classes.keySet();
iter = set.iterator();
iClass = 0;
while (iter.hasNext()) {
stats = (ArrayList) m_Classes.get(iter.next());
for (iGrid = 0; iGrid < m_Window.length; iGrid++) {
substats = ((MeanAndStdDev) stats.get(iGrid));
dMean[iClass][iGrid] = substats.mean;
dStdDev[iClass][iGrid] = substats.stdDev;
}
iClass++;
}
for (y = 0; y < iNY; y++) {
for (x = 0; x < iNX; x++) {
for (iClass = 0; iClass < m_Classes.size(); iClass++) {
iMatchingClass = iClass;
for (iGrid = 0; iGrid < m_Window.length; iGrid++) {
dValue = m_Window[iGrid].getCellValueAsDouble(x, y);
if (!m_Window[iGrid].isNoDataValue(dValue)) {
if (Math.abs(m_Window[iGrid].getCellValueAsDouble(x, y) - dMean[iClass][iGrid])
> dStdDev[iClass][iGrid]) {
iMatchingClass = -1;
break;
}
} else {
break;
}
}
if (iMatchingClass != -1) {
break;
}
}
if (iMatchingClass != -1) {
m_Output.setCellValue(x, y, iMatchingClass + 1);
} else {
m_Output.setNoData(x, y);
}
}
}
}
private void doMinimumDistance() {
int iNX, iNY;
int x, y;
int iClass, iGrid, iMin = 0;
final double dMean[][] = new double[m_Classes.size()][m_Window.length];
double dMin, d, e;
double dValue;
ArrayList stats;
Set set;
Iterator iter;
iNX = m_Output.getWindowGridExtent().getNX();
iNY = m_Output.getWindowGridExtent().getNY();
set = m_Classes.keySet();
iter = set.iterator();
iClass = 0;
while (iter.hasNext()) {
stats = (ArrayList) m_Classes.get(iter.next());
for (iGrid = 0; iGrid < m_Window.length; iGrid++) {
dMean[iClass][iGrid] = ((MeanAndStdDev) stats.get(iGrid)).mean;
}
iClass++;
}
for (y = 0; y < iNY; y++) {
for (x = 0; x < iNX; x++) {
for (iClass = 0, dMin = -1.0; iClass < m_Classes.size(); iClass++) {
for (iGrid = 0, d = 0.0; iGrid < m_Window.length; iGrid++) {
dValue = m_Window[iGrid].getCellValueAsDouble(x, y);
if (!m_Window[iGrid].isNoDataValue(dValue)) {
e = m_Window[iGrid].getCellValueAsDouble(x, y) - dMean[iClass][iGrid];
d += e * e;
if ((dMin < 0.0) || (dMin > d)) {
dMin = d;
iMin = iClass;
}
} else {
dMin = -1;
}
}
}
if (dMin >= 0.0) {
m_Output.setCellValue(x, y, iMin + 1);
} else {
m_Output.setNoData(x, y);
}
}
}
}
@Override
public boolean processAlgorithm() throws GeoAlgorithmExecutionException {
int i;
AnalysisExtent ge;
final int iMethod = m_Parameters.getParameterValueAsInt(METHOD);
m_Bands = m_Parameters.getParameterValueAsArrayList(INPUT);
if (m_Bands.size() == 0) {
return false;
}
m_Classes = new HashMap();
getClassInformation();
if (m_Task.isCanceled()) {
return false;
}
m_Output =
getNewRasterLayer(
CLASSIFICATION,
Sextante.getText("Classification"),
IRasterLayer.RASTER_DATA_TYPE_SHORT);
m_Output.setNoDataValue(-1);
ge = m_Output.getWindowGridExtent();
m_Window = new IRasterLayer[m_Bands.size()];
m_iBands = new int[m_Bands.size()];
for (i = 0; i < m_Window.length; i++) {
final RasterLayerAndBand band = (RasterLayerAndBand) m_Bands.get(i);
m_iBands[i] = band.getBand();
m_Window[i] = band.getRasterLayer();
m_Window[i].setWindowExtent(ge);
}
switch (iMethod) {
case 0:
doParalellpiped();
case 1:
default:
doMinimumDistance();
case 2:
doMaximumLikelihood();
}
return !m_Task.isCanceled();
}
private void doMaximumLikelihood() {
int iNX, iNY;
int x, y;
int iClass, iGrid, iMax = 0;
final double dMean[][] = new double[m_Classes.size()][m_Window.length];
final double dStdDev[][] = new double[m_Classes.size()][m_Window.length];
final double dK[][] = new double[m_Classes.size()][m_Window.length];
double dMax, d, e;
double dValue;
ArrayList stats;
MeanAndStdDev substats;
Set set;
Iterator iter;
iNX = m_Output.getWindowGridExtent().getNX();
iNY = m_Output.getWindowGridExtent().getNY();
set = m_Classes.keySet();
iter = set.iterator();
iClass = 0;
while (iter.hasNext()) {
stats = (ArrayList) m_Classes.get(iter.next());
for (iGrid = 0; iGrid < m_Window.length; iGrid++) {
substats = ((MeanAndStdDev) stats.get(iGrid));
dMean[iClass][iGrid] = substats.mean;
dStdDev[iClass][iGrid] = substats.stdDev;
dK[iClass][iGrid] = 1.0 / (dStdDev[iClass][iGrid] * Math.sqrt(2.0 * Math.PI));
}
iClass++;
}
for (y = 0; y < iNY; y++) {
for (x = 0; x < iNX; x++) {
for (iClass = 0, dMax = 0.0; iClass < m_Classes.size(); iClass++) {
for (iGrid = 0, d = 0.0; iGrid < m_Window.length; iGrid++) {
dValue = m_Window[iGrid].getCellValueAsDouble(x, y);
if (!m_Window[iGrid].isNoDataValue(dValue)) {
e =
(m_Window[iGrid].getCellValueAsDouble(x, y) - dMean[iClass][iGrid])
/ dStdDev[iClass][iGrid];
e = dK[iClass][iGrid] * Math.exp(-0.5 * e * e);
d += e * e;
if (dMax < d) {
dMax = d;
iMax = iClass;
}
} else {
dMax = -1;
}
}
}
if (dMax > 0.0) {
m_Output.setCellValue(x, y, iMax + 1);
} else {
m_Output.setNoData(x, y);
}
}
}
}