고유 분석

컬렉션을 사용해 정리하기 내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.

var getPrincipalComponents = function(centered, scale, region) {
  // Collapse the bands of the image into a 1D array per pixel.
  var arrays = centered.toArray();

  // Compute the covariance of the bands within the region.
  var covar = arrays.reduceRegion({
    reducer: ee.Reducer.centeredCovariance(),
    geometry: region,
    scale: scale,
    maxPixels: 1e9
  });

  // Get the 'array' covariance result and cast to an array.
  // This represents the band-to-band covariance within the region.
  var covarArray = ee.Array(covar.get('array'));

  // Perform an eigen analysis and slice apart the values and vectors.
  var eigens = covarArray.eigen();

  // This is a P-length vector of Eigenvalues.
  var eigenValues = eigens.slice(1, 0, 1);
  // This is a PxP matrix with eigenvectors in rows.
  var eigenVectors = eigens.slice(1, 1);

  // Convert the array image to 2D arrays for matrix computations.
  var arrayImage = arrays.toArray(1);

  // Left multiply the image array by the matrix of eigenvectors.
  var principalComponents = ee.Image(eigenVectors).matrixMultiply(arrayImage);

  // Turn the square roots of the Eigenvalues into a P-band image.
  var sdImage = ee.Image(eigenValues.sqrt())
      .arrayProject([0]).arrayFlatten([getNewBandNames('sd')]);

  // Turn the PCs into a P-band image, normalized by SD.
  return principalComponents
      // Throw out an an unneeded dimension, [[]] -> [].
      .arrayProject([0])
      // Make the one band array image a multi-band image, [] -> image.
      .arrayFlatten([getNewBandNames('pc')])
      // Normalize the PCs by their SDs.
      .divide(sdImage);
};

def get_principal_components(centered, scale, region):
  # Collapse bands into 1D array
  arrays = centered.toArray()

  # Compute the covariance of the bands within the region.
  covar = arrays.reduceRegion(
      reducer=ee.Reducer.centeredCovariance(),
      geometry=region,
      scale=scale,
      maxPixels=1e9,
  )

  # Get the 'array' covariance result and cast to an array.
  # This represents the band-to-band covariance within the region.
  covar_array = ee.Array(covar.get('array'))

  # Perform an eigen analysis and slice apart the values and vectors.
  eigens = covar_array.eigen()

  # This is a P-length vector of Eigenvalues.
  eigen_values = eigens.slice(1, 0, 1)
  # This is a PxP matrix with eigenvectors in rows.
  eigen_vectors = eigens.slice(1, 1)

  # Convert the array image to 2D arrays for matrix computations.
  array_image = arrays.toArray(1)

  # Left multiply the image array by the matrix of eigenvectors.
  principal_components = ee.Image(eigen_vectors).matrixMultiply(array_image)

  # Turn the square roots of the Eigenvalues into a P-band image.
  sd_image = (
      ee.Image(eigen_values.sqrt())
      .arrayProject([0])
      .arrayFlatten([get_new_band_names('sd')])
  )

  # Turn the PCs into a P-band image, normalized by SD.
  return (
      # Throw out an an unneeded dimension, [[]] -> [].
      principal_components.arrayProject([0])
      # Make the one band array image a multi-band image, [] -> image.
      .arrayFlatten([get_new_band_names('pc')])
      # Normalize the PCs by their SDs.
      .divide(sd_image)
  )