public static Matrix averageColumnVector(Matrix... matrices) {
int columns = matrices[0].getColumnDimension();
if (matrices[0].getRowDimension() != 1) throw new IllegalArgumentException();
// double[] avg = new double[columns];
Matrix avg = new Matrix(1, columns);
for (Matrix m : matrices) {
avg.plusEquals(m);
}
avg.timesEquals(1.0 / matrices.length);
return avg;
}
/**
* Update the covariance Matrix of the weight posterior distribution (SIGMA) along with its
* cholesky factor:
*
* <p>SIGMA = (A + beta * PHI^t * PHI)^{-1}
*
* <p>SIGMA_chol with SIGMA_chol * SIGMA_chol^t = SIGMA
*/
protected void updateSIGMA() {
Matrix SIGMA_inv = PHI_t.times(PHI_t.transpose());
SIGMA_inv.timesEquals(beta);
SIGMA_inv.plusEquals(A);
/** Update the factor ... */
SECholeskyDecomposition CD = new SECholeskyDecomposition(SIGMA_inv.getArray());
Matrix U = CD.getPTR().times(CD.getL());
SIGMA_chol = U.inverse();
/** Update SIGMA */
SIGMA = (SIGMA_chol.transpose()).times(SIGMA_chol);
}
/**
* Update the mean of the weight posterior distribution (mu):
*
* <p>mu = beta * SIGMA * PHI^t * t
*/
protected void updateMu() {
mu = SIGMA.times(PHI_t.times(new Matrix(t)));
mu.timesEquals(beta);
}
@Override
public final void process(final Spot spot) {
if (img.numDimensions() == 3) {
// 3D case
final SpotNeighborhood<T> neighborhood = new SpotNeighborhood<T>(spot, img);
final SpotNeighborhoodCursor<T> cursor = neighborhood.cursor();
double x, y, z;
double x2, y2, z2;
double mass, totalmass = 0;
double Ixx = 0, Iyy = 0, Izz = 0, Ixy = 0, Ixz = 0, Iyz = 0;
final double[] position = new double[img.numDimensions()];
while (cursor.hasNext()) {
cursor.fwd();
mass = cursor.get().getRealDouble();
cursor.getRelativePosition(position);
x = position[0];
y = position[1];
z = position[2];
totalmass += mass;
x2 = x * x;
y2 = y * y;
z2 = z * z;
Ixx += mass * (y2 + z2);
Iyy += mass * (x2 + z2);
Izz += mass * (x2 + y2);
Ixy -= mass * x * y;
Ixz -= mass * x * z;
Iyz -= mass * y * z;
}
final Matrix mat =
new Matrix(new double[][] {{Ixx, Ixy, Ixz}, {Ixy, Iyy, Iyz}, {Ixz, Iyz, Izz}});
mat.timesEquals(1 / totalmass);
final EigenvalueDecomposition eigdec = mat.eig();
final double[] eigenvalues = eigdec.getRealEigenvalues();
final Matrix eigenvectors = eigdec.getV();
final double I1 = eigenvalues[0];
final double I2 = eigenvalues[1];
final double I3 = eigenvalues[2];
final double a = Math.sqrt(2.5 * (I2 + I3 - I1));
final double b = Math.sqrt(2.5 * (I3 + I1 - I2));
final double c = Math.sqrt(2.5 * (I1 + I2 - I3));
final double[] semiaxes = new double[] {a, b, c};
// Sort semi-axes by ascendent order and get the sorting index
final double[] semiaxes_ordered = semiaxes.clone();
Arrays.sort(semiaxes_ordered);
final int[] order = new int[3];
for (int i = 0; i < semiaxes_ordered.length; i++)
for (int j = 0; j < semiaxes.length; j++)
if (semiaxes_ordered[i] == semiaxes[j]) order[i] = j;
// Get the sorted eigenvalues
final double[][] uvectors = new double[3][3];
for (int i = 0; i < eigenvalues.length; i++) {
uvectors[i][0] = eigenvectors.get(0, order[i]);
uvectors[i][1] = eigenvectors.get(1, order[i]);
uvectors[i][2] = eigenvectors.get(2, order[i]);
}
// Store in the Spot object
double theta, phi;
for (int i = 0; i < uvectors.length; i++) {
theta =
Math.acos(
uvectors[i][2]
/ Math.sqrt(
uvectors[i][0] * uvectors[i][0]
+ uvectors[i][1] * uvectors[i][1]
+ uvectors[i][2] * uvectors[i][2]));
phi = Math.atan2(uvectors[i][1], uvectors[i][0]);
if (phi < -Math.PI / 2) phi += Math.PI; // For an ellipsoid we care only for the
// angles in [-pi/2 , pi/2]
if (phi > Math.PI / 2) phi -= Math.PI;
// Store in descending order
spot.putFeature(featurelist_sa[i], semiaxes_ordered[i]);
spot.putFeature(featurelist_phi[i], phi);
spot.putFeature(featurelist_theta[i], theta);
}
// Store the Spot morphology (needs to be outside the above loop)
spot.putFeature(MORPHOLOGY, estimateMorphology(semiaxes_ordered));
} else if (img.numDimensions() == 2) {
// 2D case
final SpotNeighborhood<T> neighborhood = new SpotNeighborhood<T>(spot, img);
final SpotNeighborhoodCursor<T> cursor = neighborhood.cursor();
double x, y;
double x2, y2;
double mass, totalmass = 0;
double Ixx = 0, Iyy = 0, Ixy = 0;
final double[] position = new double[img.numDimensions()];
while (cursor.hasNext()) {
cursor.fwd();
mass = cursor.get().getRealDouble();
cursor.getRelativePosition(position);
x = position[0];
y = position[1];
totalmass += mass;
x2 = x * x;
y2 = y * y;
Ixx += mass * (y2);
Iyy += mass * (x2);
Ixy -= mass * x * y;
}
final Matrix mat = new Matrix(new double[][] {{Ixx, Ixy}, {Ixy, Iyy}});
mat.timesEquals(1 / totalmass);
final EigenvalueDecomposition eigdec = mat.eig();
final double[] eigenvalues = eigdec.getRealEigenvalues();
final Matrix eigenvectors = eigdec.getV();
final double I1 = eigenvalues[0];
final double I2 = eigenvalues[1];
final double a = Math.sqrt(4 * I1);
final double b = Math.sqrt(4 * I2);
final double[] semiaxes = new double[] {a, b};
// Sort semi-axes by ascendent order and get the sorting index
final double[] semiaxes_ordered = semiaxes.clone();
Arrays.sort(semiaxes_ordered);
final int[] order = new int[2];
for (int i = 0; i < semiaxes_ordered.length; i++)
for (int j = 0; j < semiaxes.length; j++)
if (semiaxes_ordered[i] == semiaxes[j]) order[i] = j;
// Get the sorted eigenvalues
final double[][] uvectors = new double[2][2];
for (int i = 0; i < eigenvalues.length; i++) {
uvectors[i][0] = eigenvectors.get(0, order[i]);
uvectors[i][1] = eigenvectors.get(1, order[i]);
}
// Store in the Spot object
double theta, phi;
for (int i = 0; i < uvectors.length; i++) {
theta = 0;
phi = Math.atan2(uvectors[i][1], uvectors[i][0]);
if (phi < -Math.PI / 2) phi += Math.PI; // For an ellipsoid we care only for the
// angles in [-pi/2 , pi/2]
if (phi > Math.PI / 2) phi -= Math.PI;
// Store in descending order
spot.putFeature(featurelist_sa[i], semiaxes_ordered[i]);
spot.putFeature(featurelist_phi[i], phi);
spot.putFeature(featurelist_theta[i], theta);
}
spot.putFeature(featurelist_sa[2], Double.valueOf(0));
spot.putFeature(featurelist_phi[2], Double.valueOf(0));
spot.putFeature(featurelist_theta[2], Double.valueOf(0));
// Store the Spot morphology (needs to be outside the above loop)
spot.putFeature(MORPHOLOGY, estimateMorphology(semiaxes_ordered));
}
}
/**
* Get mean covariance matrix C2 for given pixel.
*
* @param x X coordinate of the given pixel.
* @param y Y coordinate of the given pixel.
* @param halfWindowSizeX The sliding window width /2
* @param halfWindowSizeY The sliding window height /2
* @param sourceImageWidth Source image width.
* @param sourceImageHeight Source image height.
* @param sourceProductType The source product type.
* @param sourceTiles The source tiles for all bands.
* @param dataBuffers Source tile data buffers.
* @param Cr The real part of the mean covariance matrix.
* @param Ci The imaginary part of the mean covariance matrix.
*/
public static void getMeanCovarianceMatrixC2(
final int x,
final int y,
final int halfWindowSizeX,
final int halfWindowSizeY,
final int sourceImageWidth,
final int sourceImageHeight,
final PolBandUtils.MATRIX sourceProductType,
final Tile[] sourceTiles,
final ProductData[] dataBuffers,
final double[][] Cr,
final double[][] Ci) {
final double[][] tempCr = new double[2][2];
final double[][] tempCi = new double[2][2];
final int xSt = Math.max(x - halfWindowSizeX, 0);
final int xEd = Math.min(x + halfWindowSizeX, sourceImageWidth - 1);
final int ySt = Math.max(y - halfWindowSizeY, 0);
final int yEd = Math.min(y + halfWindowSizeY, sourceImageHeight - 1);
final int num = (yEd - ySt + 1) * (xEd - xSt + 1);
final TileIndex srcIndex = new TileIndex(sourceTiles[0]);
final Matrix CrMat = new Matrix(2, 2);
final Matrix CiMat = new Matrix(2, 2);
if (sourceProductType == PolBandUtils.MATRIX.C2) {
for (int yy = ySt; yy <= yEd; ++yy) {
srcIndex.calculateStride(yy);
for (int xx = xSt; xx <= xEd; ++xx) {
getCovarianceMatrixC2(srcIndex.getIndex(xx), dataBuffers, tempCr, tempCi);
CrMat.plusEquals(new Matrix(tempCr));
CiMat.plusEquals(new Matrix(tempCi));
}
}
} else if (sourceProductType == PolBandUtils.MATRIX.LCHCP
|| sourceProductType == PolBandUtils.MATRIX.RCHCP
|| sourceProductType == PolBandUtils.MATRIX.DUAL_HH_HV
|| sourceProductType == PolBandUtils.MATRIX.DUAL_VH_VV
|| sourceProductType == PolBandUtils.MATRIX.DUAL_HH_VV) {
final double[] tempKr = new double[2];
final double[] tempKi = new double[2];
for (int yy = ySt; yy <= yEd; ++yy) {
srcIndex.calculateStride(yy);
for (int xx = xSt; xx <= xEd; ++xx) {
getScatterVector(srcIndex.getIndex(xx), dataBuffers, tempKr, tempKi);
computeCovarianceMatrixC2(tempKr, tempKi, tempCr, tempCi);
CrMat.plusEquals(new Matrix(tempCr));
CiMat.plusEquals(new Matrix(tempCi));
}
}
} else {
throw new OperatorException("getMeanCovarianceMatrixC2 not implemented for raw dual pol");
}
CrMat.timesEquals(1.0 / num);
CiMat.timesEquals(1.0 / num);
for (int i = 0; i < 2; i++) {
Cr[i][0] = CrMat.get(i, 0);
Ci[i][0] = CiMat.get(i, 0);
Cr[i][1] = CrMat.get(i, 1);
Ci[i][1] = CiMat.get(i, 1);
}
}
