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Update PCA Code Editor script link (Landsat C1 to C2)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,176 @@ | ||
| { | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0, | ||
| "metadata": { | ||
| "colab": { | ||
| "provenance": [] | ||
| }, | ||
| "kernelspec": { | ||
| "name": "python3", | ||
| "display_name": "Python 3" | ||
| }, | ||
| "language_info": { | ||
| "name": "python" | ||
| } | ||
| }, | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "metadata": { | ||
| "id": "fSIfBsgi8dNK" | ||
| }, | ||
| "source": [ | ||
| "#@title Copyright 2024 Google LLC. { display-mode: \"form\" }\n", | ||
| "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", | ||
| "# you may not use this file except in compliance with the License.\n", | ||
| "# You may obtain a copy of the License at\n", | ||
| "#\n", | ||
| "# https://www.apache.org/licenses/LICENSE-2.0\n", | ||
| "#\n", | ||
| "# Unless required by applicable law or agreed to in writing, software\n", | ||
| "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", | ||
| "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", | ||
| "# See the License for the specific language governing permissions and\n", | ||
| "# limitations under the License." | ||
| ], | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "id": "aV1xZ1CPi3Nw" | ||
| }, | ||
| "source": [ | ||
| "<table class=\"ee-notebook-buttons\" align=\"left\"><td>\n", | ||
| "<a target=\"_blank\" href=\"http://colab.research.google.com/github/google/earthengine-community/blob/master/guides/linked/Earth_Engine_PCA.ipynb\">\n", | ||
| " <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a>\n", | ||
| "</td><td>\n", | ||
| "<a target=\"_blank\" href=\"https://github.com/google/earthengine-community/blob/master/guides/linked/Earth_Engine_AutoML_Vertex_AI.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td></table>\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "import ee\n", | ||
| "import geemap\n", | ||
| "\n", | ||
| "ee.Authenticate()\n", | ||
| "ee.Initialize(project='MY-PROJECT')" | ||
| ], | ||
| "metadata": { | ||
| "id": "B66Qy8ems9nT" | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "# Use these bands.\n", | ||
| "band_names = ee.List(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'B11'])\n", | ||
| "\n", | ||
| "# Load a landsat 8 image and select the bands of interest.\n", | ||
| "image = ee.Image('LANDSAT/LC08/C02/T1/LC08_044034_20140318').select(band_names)\n", | ||
| "\n", | ||
| "# Display the input imagery and the region in which to do the PCA.\n", | ||
| "region = image.geometry()\n", | ||
| "m = geemap.Map()\n", | ||
| "m.center_object(region, 10)\n", | ||
| "m.add_layer(ee.Image().paint(region, 0, 2), {}, 'Region')\n", | ||
| "m.add_layer(\n", | ||
| " image,\n", | ||
| " {'bands': ['B5', 'B4', 'B2'], 'min': 0, 'max': 20000},\n", | ||
| " 'Original Image',\n", | ||
| ")\n", | ||
| "display(m)\n", | ||
| "\n", | ||
| "# Set an appropriate scale for Landsat data.\n", | ||
| "scale = 30\n", | ||
| "\n", | ||
| "# Mean center the data to enable a faster covariance reducer\n", | ||
| "# and an SD stretch of the principal components.\n", | ||
| "mean_dict = image.reduceRegion(\n", | ||
| " reducer=ee.Reducer.mean(), geometry=region, scale=scale, maxPixels=1e9\n", | ||
| ")\n", | ||
| "means = mean_dict.toImage(band_names)\n", | ||
| "centered = image.subtract(means)\n", | ||
| "\n", | ||
| "\n", | ||
| "# This helper function returns a list of new band names.\n", | ||
| "def get_new_band_names(prefix):\n", | ||
| " seq = ee.List.sequence(1, band_names.length())\n", | ||
| "\n", | ||
| " def add_prefix_and_number(b):\n", | ||
| " return ee.String(prefix).cat(ee.Number(b).int())\n", | ||
| "\n", | ||
| " return seq.map(add_prefix_and_number)\n", | ||
| "\n", | ||
| "\n", | ||
| "# This function accepts mean centered imagery, a scale and\n", | ||
| "# a region in which to perform the analysis. It returns the\n", | ||
| "# Principal Components (PC) in the region as a new image.\n", | ||
| "def get_principal_components(centered, scale, region):\n", | ||
| " # Collapse bands into 1D array\n", | ||
| " arrays = centered.toArray()\n", | ||
| "\n", | ||
| " # Compute the covariance of the bands within the region.\n", | ||
| " covar = arrays.reduceRegion(\n", | ||
| " reducer=ee.Reducer.centeredCovariance(),\n", | ||
| " geometry=region,\n", | ||
| " scale=scale,\n", | ||
| " maxPixels=1e9,\n", | ||
| " )\n", | ||
| "\n", | ||
| " # Get the 'array' covariance result and cast to an array.\n", | ||
| " # This represents the band-to-band covariance within the region.\n", | ||
| " covar_array = ee.Array(covar.get('array'))\n", | ||
| "\n", | ||
| " # Perform an eigen analysis and slice apart the values and vectors.\n", | ||
| " eigens = covar_array.eigen()\n", | ||
| "\n", | ||
| " # This is a P-length vector of Eigenvalues.\n", | ||
| " eigen_values = eigens.slice(1, 0, 1)\n", | ||
| " # This is a PxP matrix with eigenvectors in rows.\n", | ||
| " eigen_vectors = eigens.slice(1, 1)\n", | ||
| "\n", | ||
| " # Convert the array image to 2D arrays for matrix computations.\n", | ||
| " array_image = arrays.toArray(1)\n", | ||
| "\n", | ||
| " # Left multiply the image array by the matrix of eigenvectors.\n", | ||
| " principal_components = ee.Image(eigen_vectors).matrixMultiply(array_image)\n", | ||
| "\n", | ||
| " # Turn the square roots of the Eigenvalues into a P-band image.\n", | ||
| " sd_image = (\n", | ||
| " ee.Image(eigen_values.sqrt())\n", | ||
| " .arrayProject([0])\n", | ||
| " .arrayFlatten([get_new_band_names('sd')])\n", | ||
| " )\n", | ||
| "\n", | ||
| " # Turn the PCs into a P-band image, normalized by SD.\n", | ||
| " return (\n", | ||
| " # Throw out an an unneeded dimension, [[]] -> [].\n", | ||
| " principal_components.arrayProject([0])\n", | ||
| " # Make the one band array image a multi-band image, [] -> image.\n", | ||
| " .arrayFlatten([get_new_band_names('pc')])\n", | ||
| " # Normalize the PCs by their SDs.\n", | ||
| " .divide(sd_image)\n", | ||
| " )\n", | ||
| "\n", | ||
| "\n", | ||
| "\n", | ||
| "# Get the PCs at the specified scale and in the specified region\n", | ||
| "pc_image = get_principal_components(centered, scale, region)\n", | ||
| "\n", | ||
| "# Plot each PC as a new layer\n", | ||
| "for i, band in enumerate(pc_image.bandNames().getInfo()):\n", | ||
| " m.add_layer(pc_image.select([band]), {'min': -2, 'max': 2}, band)" | ||
| ], | ||
| "metadata": { | ||
| "id": "MOVk7mp1s1ma" | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| } | ||
| ] | ||
| } |
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