/**
* Compute the centers of the 90 clusters in the 3 categories.
*
* @param srcBandList the input bands
* @param tileRectangles Array of rectangles for all source tiles of the image
* @param op the operator
*/
private void getClusterCenters(
final java.util.List<ClusterInfo> pvCenterList,
final java.util.List<ClusterInfo> pdCenterList,
final java.util.List<ClusterInfo> psCenterList,
final PolBandUtils.PolSourceBand srcBandList,
final Rectangle[] tileRectangles,
final PolarimetricClassificationOp op) {
final StatusProgressMonitor status =
new StatusProgressMonitor(StatusProgressMonitor.TYPE.SUBTASK);
status.beginTask("Computing Initial Cluster Centres... ", tileRectangles.length);
final ThreadManager threadManager = new ThreadManager();
final double[][][] pvSumRe = new double[numInitialClusters][3][3];
final double[][][] pvSumIm = new double[numInitialClusters][3][3];
final double[][][] pdSumRe = new double[numInitialClusters][3][3];
final double[][][] pdSumIm = new double[numInitialClusters][3][3];
final double[][][] psSumRe = new double[numInitialClusters][3][3];
final double[][][] psSumIm = new double[numInitialClusters][3][3];
// counter for the number of pixels in each cluster in the 3 categories: vol, dbl, suf
final int[][] clusterCounter = new int[3][numInitialClusters];
try {
for (final Rectangle rectangle : tileRectangles) {
op.checkIfCancelled();
final Thread worker =
new Thread() {
final Tile[] sourceTiles = new Tile[srcBandList.srcBands.length];
final ProductData[] dataBuffers = new ProductData[srcBandList.srcBands.length];
final double[][] Sr = new double[2][2];
final double[][] Si = new double[2][2];
final double[][] Tr = new double[3][3];
final double[][] Ti = new double[3][3];
@Override
public void run() {
final int x0 = rectangle.x;
final int y0 = rectangle.y;
final int w = rectangle.width;
final int h = rectangle.height;
final int xMax = x0 + w;
final int yMax = y0 + h;
// System.out.println("x0 = " + x0 + ", y0 = " + y0 + ", w = " + w + ", h = " + h);
for (int i = 0; i < sourceTiles.length; ++i) {
sourceTiles[i] = op.getSourceTile(srcBandList.srcBands[i], rectangle);
dataBuffers[i] = sourceTiles[i].getDataBuffer();
}
final TileIndex srcIndex = new TileIndex(sourceTiles[0]);
for (int y = y0; y < yMax; ++y) {
srcIndex.calculateStride(y);
for (int x = x0; x < xMax; ++x) {
PolOpUtils.getT3(srcIndex.getIndex(x), sourceProductType, dataBuffers, Tr, Ti);
synchronized (clusterCounter) {
int clusterIdx;
if (mask[y][x] < -64) { // pv
clusterIdx = mask[y][x] + 128;
computeSummationOfT3(clusterIdx + 1, Tr, Ti, pvSumRe, pvSumIm);
clusterCounter[0][clusterIdx]++;
} else if (mask[y][x] < 0) { // pd
clusterIdx = mask[y][x] + 64;
computeSummationOfT3(clusterIdx + 1, Tr, Ti, pdSumRe, pdSumIm);
clusterCounter[1][clusterIdx]++;
} else if (mask[y][x] < 64) { // ps
clusterIdx = mask[y][x];
computeSummationOfT3(clusterIdx + 1, Tr, Ti, psSumRe, psSumIm);
clusterCounter[2][clusterIdx]++;
}
}
}
}
}
};
threadManager.add(worker);
status.worked(1);
}
threadManager.finish();
} catch (Throwable e) {
OperatorUtils.catchOperatorException(op.getId() + " getClusterCenters ", e);
} finally {
status.done();
}
// compute centers for all 90 clusters
for (int c = 0; c < numInitialClusters; c++) {
double[][] centerRe = new double[3][3];
double[][] centerIm = new double[3][3];
if (clusterCounter[0][c] > 0) {
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
centerRe[i][j] = pvSumRe[c][i][j] / clusterCounter[0][c];
centerIm[i][j] = pvSumIm[c][i][j] / clusterCounter[0][c];
}
}
ClusterInfo clusterCenter = new ClusterInfo();
clusterCenter.setClusterCenter(c, centerRe, centerIm, clusterCounter[0][c]);
pvCenterList.add(clusterCenter);
}
if (clusterCounter[1][c] > 0) {
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
centerRe[i][j] = pdSumRe[c][i][j] / clusterCounter[1][c];
centerIm[i][j] = pdSumIm[c][i][j] / clusterCounter[1][c];
}
}
ClusterInfo clusterCenter = new ClusterInfo();
clusterCenter.setClusterCenter(c, centerRe, centerIm, clusterCounter[1][c]);
pdCenterList.add(clusterCenter);
}
if (clusterCounter[2][c] > 0) {
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
centerRe[i][j] = psSumRe[c][i][j] / clusterCounter[2][c];
centerIm[i][j] = psSumIm[c][i][j] / clusterCounter[2][c];
}
}
ClusterInfo clusterCenter = new ClusterInfo();
clusterCenter.setClusterCenter(c, centerRe, centerIm, clusterCounter[2][c]);
psCenterList.add(clusterCenter);
}
}
}
/**
* Compute final cluster centers for all clusters using K-mean clustering method
*
* @param srcBandList the input bands
* @param tileRectangles Array of rectangles for all source tiles of the image
* @param op the operator
*/
private void computeFinalTerrainClusterCenters(
final double[][] fdd,
final java.util.List<ClusterInfo> pvCenterList,
final java.util.List<ClusterInfo> pdCenterList,
final java.util.List<ClusterInfo> psCenterList,
final PolBandUtils.PolSourceBand srcBandList,
final Rectangle[] tileRectangles,
final PolarimetricClassificationOp op) {
boolean endIteration = false;
final StatusProgressMonitor status =
new StatusProgressMonitor(StatusProgressMonitor.TYPE.SUBTASK);
status.beginTask("Computing Final Cluster Centres... ", tileRectangles.length * maxIterations);
final int pvNumClusters = pvCenterList.size();
final int pdNumClusters = pdCenterList.size();
final int psNumClusters = psCenterList.size();
final int maxNumClusters = Math.max(pvNumClusters, Math.max(pdNumClusters, psNumClusters));
final int[][] clusterCounter = new int[3][maxNumClusters];
final ThreadManager threadManager = new ThreadManager();
try {
for (int it = 0; (it < maxIterations && !endIteration); ++it) {
// System.out.println("Iteration: " + it);
// final long startTime = System.nanoTime();
// final long endTime;
final double[][][] pvSumRe = new double[pvNumClusters][3][3];
final double[][][] pvSumIm = new double[pvNumClusters][3][3];
final double[][][] pdSumRe = new double[pdNumClusters][3][3];
final double[][][] pdSumIm = new double[pdNumClusters][3][3];
final double[][][] psSumRe = new double[psNumClusters][3][3];
final double[][][] psSumIm = new double[psNumClusters][3][3];
java.util.Arrays.fill(clusterCounter[0], 0);
java.util.Arrays.fill(clusterCounter[1], 0);
java.util.Arrays.fill(clusterCounter[2], 0);
for (final Rectangle rectangle : tileRectangles) {
final Thread worker =
new Thread() {
final Tile[] sourceTiles = new Tile[srcBandList.srcBands.length];
final ProductData[] dataBuffers = new ProductData[srcBandList.srcBands.length];
final double[][] Tr = new double[3][3];
final double[][] Ti = new double[3][3];
@Override
public void run() {
op.checkIfCancelled();
final int x0 = rectangle.x;
final int y0 = rectangle.y;
final int w = rectangle.width;
final int h = rectangle.height;
final int xMax = x0 + w;
final int yMax = y0 + h;
final Rectangle sourceRectangle = getSourceRectangle(x0, y0, w, h);
for (int i = 0; i < sourceTiles.length; ++i) {
sourceTiles[i] = op.getSourceTile(srcBandList.srcBands[i], sourceRectangle);
dataBuffers[i] = sourceTiles[i].getDataBuffer();
}
final TileIndex srcIndex = new TileIndex(sourceTiles[0]);
for (int y = y0; y < yMax; ++y) {
for (int x = x0; x < xMax; ++x) {
PolOpUtils.getMeanCoherencyMatrix(
x,
y,
halfWindowSizeX,
halfWindowSizeY,
srcWidth,
srcHeight,
sourceProductType,
srcIndex,
dataBuffers,
Tr,
Ti);
int clusterIdx;
synchronized (clusterCounter) {
if (mask[y][x] < -64) { // pv
clusterIdx = findClosestCluster(Tr, Ti, pvCenterList);
computeSummationOfT3(clusterIdx + 1, Tr, Ti, pvSumRe, pvSumIm);
clusterCounter[0][clusterIdx] += 1;
mask[y][x] = (byte) (-128 + clusterIdx);
} else if (mask[y][x] < 0) { // pd
clusterIdx = findClosestCluster(Tr, Ti, pdCenterList);
computeSummationOfT3(clusterIdx + 1, Tr, Ti, pdSumRe, pdSumIm);
clusterCounter[1][clusterIdx] += 1;
mask[y][x] = (byte) (-64 + clusterIdx);
} else if (mask[y][x] < 64) { // ps
clusterIdx = findClosestCluster(Tr, Ti, psCenterList);
computeSummationOfT3(clusterIdx + 1, Tr, Ti, psSumRe, psSumIm);
clusterCounter[2][clusterIdx] += 1;
mask[y][x] = (byte) clusterIdx;
} else { // mixed
java.util.List<ClusterInfo> allCenterList = new ArrayList<>();
allCenterList.addAll(pvCenterList);
allCenterList.addAll(pdCenterList);
allCenterList.addAll(psCenterList);
clusterIdx = findClosestCluster(Tr, Ti, allCenterList);
if (clusterIdx >= pvNumClusters + pdNumClusters) { // ps
clusterIdx -= pvNumClusters + pdNumClusters;
computeSummationOfT3(clusterIdx + 1, Tr, Ti, psSumRe, psSumIm);
clusterCounter[2][clusterIdx] += 1;
mask[y][x] = (byte) clusterIdx;
} else if (clusterIdx >= pvNumClusters) { // pd
clusterIdx -= pvNumClusters;
computeSummationOfT3(clusterIdx + 1, Tr, Ti, pdSumRe, pdSumIm);
clusterCounter[1][clusterIdx] += 1;
mask[y][x] = (byte) (-64 + clusterIdx);
} else { // pv
computeSummationOfT3(clusterIdx + 1, Tr, Ti, pvSumRe, pvSumIm);
clusterCounter[0][clusterIdx] += 1;
mask[y][x] = (byte) (-128 + clusterIdx);
}
}
}
}
}
}
};
threadManager.add(worker);
status.worked(1);
}
threadManager.finish();
/*
endTime = System.nanoTime();
final long duration = endTime - startTime;
System.out.println("duration = " + duration);
*/
updateClusterCenter(pvCenterList, clusterCounter[0], pvSumRe, pvSumIm);
updateClusterCenter(pdCenterList, clusterCounter[1], pdSumRe, pdSumIm);
updateClusterCenter(psCenterList, clusterCounter[2], psSumRe, psSumIm);
}
/*
System.out.println("# of clusters in Pv: " + pvNumClusters);
System.out.print("Pixels in each Pv cluster: ");
for (int i = 0; i < pvNumClusters; i++) {
System.out.print(clusterCounter[0][i] + ", ");
}
System.out.println();
System.out.println("# of clusters in Pd: " + pdNumClusters);
System.out.print("Pixels in each Pd cluster: ");
for (int i = 0; i < pdNumClusters; i++) {
System.out.print(clusterCounter[1][i] + ", ");
}
System.out.println();
System.out.println("# of clusters in Ps: " + psNumClusters);
System.out.print("Pixels in each Ps cluster: ");
for (int i = 0; i < psNumClusters; i++) {
System.out.print(clusterCounter[2][i] + ", ");
}
System.out.println();
*/
// todo the average cluster power should be used in colour selection for each cluster, not
// used now
// compute average power for each cluster
final double[] pvAvgClusterPower = new double[pvNumClusters];
final double[] pdAvgClusterPower = new double[pdNumClusters];
final double[] psAvgClusterPower = new double[psNumClusters];
int clusterIdx = -1;
for (int y = 0; y < srcHeight; y++) {
for (int x = 0; x < srcWidth; x++) {
if (mask[y][x] < -64) { // pv
clusterIdx = mask[y][x] + 128;
pvAvgClusterPower[clusterIdx] += fdd[y][x];
} else if (mask[y][x] < 0) { // pd
clusterIdx = mask[y][x] + 64;
pdAvgClusterPower[clusterIdx] += fdd[y][x];
} else { // ps
clusterIdx = mask[y][x];
psAvgClusterPower[clusterIdx] += fdd[y][x];
}
}
}
for (int c = 0; c < pvNumClusters; c++) {
pvAvgClusterPower[c] /= clusterCounter[0][c];
}
for (int c = 0; c < pdNumClusters; c++) {
pdAvgClusterPower[c] /= clusterCounter[1][c];
}
for (int c = 0; c < psNumClusters; c++) {
psAvgClusterPower[c] /= clusterCounter[2][c];
}
// map cluster index to colour index, colour index ranges for the 3 categories are given by
// surf: [0,29], vol: [30, 59] and dbl: [60,89], currently the clusters are evenly distributed
// in the
// colour range
pvColourIndexMap = new int[pvNumClusters];
pdColourIndexMap = new int[pdNumClusters];
psColourIndexMap = new int[psNumClusters];
for (int c = 0; c < pvNumClusters; c++) {
pvColourIndexMap[c] =
numInitialClusters + getColourIndex(c, pvAvgClusterPower, numInitialClusters) + 1;
}
for (int c = 0; c < pdNumClusters; c++) {
pdColourIndexMap[c] =
2 * numInitialClusters + getColourIndex(c, pdAvgClusterPower, numInitialClusters) + 1;
}
for (int c = 0; c < psNumClusters; c++) {
psColourIndexMap[c] = getColourIndex(c, psAvgClusterPower, numInitialClusters) + 1;
}
} catch (Throwable e) {
OperatorUtils.catchOperatorException(op.getId() + " computeInitialClusterCenters ", e);
} finally {
status.done();
}
}
/**
* Create 30 initial clusters in each of the 3 categories (vol, dbl and surf). The pixels are
* first classified into 4 categories (vol, dbl, urf and mixed) based on its Freeman-Durder
* decomposition result. Then pixels in each category (not include mixed) are grouped into 30
* clusters based on their power values.
*
* @param srcBandList the input bands
* @param tileRectangles Array of rectangles for all source tiles of the image
* @param op the operator
*/
private void createInitialClusters(
final double[][] fdd,
final PolBandUtils.PolSourceBand srcBandList,
final Rectangle[] tileRectangles,
final PolarimetricClassificationOp op) {
// Here mask[][] is used in recording the category index for each pixel with -128 for vol, -64
// for dbl,
// 0 for surf and 64 for mixed. Later mask[][] will be used in recording for each pixel the
// cluster index
// in each category. [-128, -63] are for clusters in vol category, [-64, -1] are for clusters in
// dbl
// category and [0, 63] are for clusters in surf category.
//
// fdd[][] is used in recording the dominant power of the Freeman-Durden decomposition result
// for each
// pixel.
final StatusProgressMonitor status =
new StatusProgressMonitor(StatusProgressMonitor.TYPE.SUBTASK);
status.beginTask("Creating Initial Clusters... ", tileRectangles.length);
final int[] counter =
new int[4]; // number of pixels in each of the 4 categories: vol, dbl, suf, mix
final ThreadManager threadManager = new ThreadManager();
final double[] pv = new double[srcHeight * srcWidth];
final double[] pd = new double[srcHeight * srcWidth];
final double[] ps = new double[srcHeight * srcWidth];
try {
for (final Rectangle rectangle : tileRectangles) {
op.checkIfCancelled();
final Thread worker =
new Thread() {
final Tile[] sourceTiles = new Tile[srcBandList.srcBands.length];
final ProductData[] dataBuffers = new ProductData[srcBandList.srcBands.length];
final double[][] Cr = new double[3][3];
final double[][] Ci = new double[3][3];
@Override
public void run() {
final int x0 = rectangle.x;
final int y0 = rectangle.y;
final int w = rectangle.width;
final int h = rectangle.height;
final int xMax = x0 + w;
final int yMax = y0 + h;
// System.out.println("x0 = " + x0 + ", y0 = " + y0 + ", w = " + w + ", h = " + h);
final Rectangle sourceRectangle = getSourceRectangle(x0, y0, w, h);
for (int i = 0; i < sourceTiles.length; ++i) {
sourceTiles[i] = op.getSourceTile(srcBandList.srcBands[i], sourceRectangle);
dataBuffers[i] = sourceTiles[i].getDataBuffer();
}
for (int y = y0; y < yMax; ++y) {
for (int x = x0; x < xMax; ++x) {
PolOpUtils.getMeanCovarianceMatrix(
x,
y,
halfWindowSizeX,
halfWindowSizeY,
sourceProductType,
sourceTiles,
dataBuffers,
Cr,
Ci);
final FreemanDurden.FDD data =
FreemanDurden.getFreemanDurdenDecomposition(Cr, Ci);
synchronized (counter) {
if (!Double.isNaN(data.pv)
&& !Double.isNaN(data.pd)
&& !Double.isNaN(data.ps)) {
Categories cat =
getCategory(data.pv, data.pd, data.ps, mixedCategoryThreshold);
if (cat == Categories.vol) {
mask[y][x] = -128;
fdd[y][x] = data.pv;
pv[counter[0]] = data.pv;
counter[0] += 1;
} else if (cat == Categories.dbl) {
mask[y][x] = -64;
fdd[y][x] = data.pd;
pd[counter[1]] = data.pd;
counter[1] += 1;
} else if (cat == Categories.suf) {
mask[y][x] = 0;
fdd[y][x] = data.ps;
ps[counter[2]] = data.ps;
counter[2] += 1;
} else { // cat == Categories.mix
mask[y][x] = 64;
fdd[y][x] = (data.pv + data.pd + data.ps) / 3.0;
counter[3] += 1;
}
}
}
}
}
}
};
threadManager.add(worker);
status.worked(1);
}
threadManager.finish();
} catch (Throwable e) {
OperatorUtils.catchOperatorException(op.getId() + " createInitialClusters ", e);
} finally {
status.done();
}
// for each category, compute 29 thresholds which will be used later in dividing each category
// into 30 small
// clusters with roughly equal number of pixels based on the pixel values.
final int pvClusterSize = counter[0] / numInitialClusters;
final int pdClusterSize = counter[1] / numInitialClusters;
final int psClusterSize = counter[2] / numInitialClusters;
if (pvClusterSize > 0) {
Arrays.sort(pv, 0, counter[0] - 1);
}
if (pdClusterSize > 0) {
Arrays.sort(pd, 0, counter[1] - 1);
}
if (psClusterSize > 0) {
Arrays.sort(ps, 0, counter[2] - 1);
}
final double[] pvThreshold = new double[numInitialClusters - 1];
final double[] pdThreshold = new double[numInitialClusters - 1];
final double[] psThreshold = new double[numInitialClusters - 1];
for (int i = 0; i < numInitialClusters - 1; i++) {
pvThreshold[i] = pv[(i + 1) * pvClusterSize];
pdThreshold[i] = pd[(i + 1) * pdClusterSize];
psThreshold[i] = ps[(i + 1) * psClusterSize];
}
// classify pixels into 30 clusters within each category, record number of pixels in each
// cluster
int clusterIdx = -1;
for (int y = 0; y < srcHeight; y++) {
for (int x = 0; x < srcWidth; x++) {
if (mask[y][x] == -128) {
clusterIdx = computePixelClusterIdx(fdd[y][x], pvThreshold, numInitialClusters);
mask[y][x] += clusterIdx;
} else if (mask[y][x] == -64) {
clusterIdx = computePixelClusterIdx(fdd[y][x], pdThreshold, numInitialClusters);
mask[y][x] += clusterIdx;
} else if (mask[y][x] == 0) {
clusterIdx = computePixelClusterIdx(fdd[y][x], psThreshold, numInitialClusters);
mask[y][x] += clusterIdx;
}
}
}
}
