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Filtrare una raccolta di immagini
Mantieni tutto organizzato con le raccolte
Salva e classifica i contenuti in base alle tue preferenze.
Come illustrato nella sezione Inizia
e nella sezione Informazioni sulle raccolte di immagini, Earth
Engine fornisce una serie di metodi di utilità per filtrare le raccolte di immagini.
Nello specifico, molti casi d'uso comuni sono gestiti da imageCollection.filterDate(),
e imageCollection.filterBounds(). Per i filtri generici, utilizza
imageCollection.filter() con un ee.Filter come argomento. L'esempio seguente mostra sia i metodi di praticità sia filter() per identificare e rimuovere le immagini con copertura nuvolosa elevata da un ImageCollection.
Editor di codice (JavaScript)
// Load Landsat 8 data, filter by date, month, and bounds.
var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
.filterDate('2015-01-01', '2018-01-01') // Three years of data
.filter(ee.Filter.calendarRange(11, 2, 'month')) // Only Nov-Feb observations
.filterBounds(ee.Geometry.Point(25.8544, -18.08874)); // Intersecting ROI
// Also filter the collection by the CLOUD_COVER property.
var filtered = collection.filter(ee.Filter.eq('CLOUD_COVER', 0));
// Create two composites to check the effect of filtering by CLOUD_COVER.
var badComposite = collection.mean();
var goodComposite = filtered.mean();
// Display the composites.
Map.setCenter(25.8544, -18.08874, 13);
Map.addLayer(badComposite,
{bands: ['B3', 'B2', 'B1'], min: 0.05, max: 0.35, gamma: 1.1},
'Bad composite');
Map.addLayer(goodComposite,
{bands: ['B3', 'B2', 'B1'], min: 0.05, max: 0.35, gamma: 1.1},
'Good composite');
Configurazione di Python
Per informazioni sull'API Python e sull'utilizzo di geemap per lo sviluppo interattivo, consulta la pagina
Ambiente Python.
import ee
import geemap.core as geemap
Colab (Python)
# Load Landsat 8 data, filter by date, month, and bounds.
collection = (
ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
# Three years of data
.filterDate('2015-01-01', '2018-01-01')
# Only Nov-Feb observations
.filter(ee.Filter.calendarRange(11, 2, 'month'))
# Intersecting ROI
.filterBounds(ee.Geometry.Point(25.8544, -18.08874))
)
# Also filter the collection by the CLOUD_COVER property.
filtered = collection.filter(ee.Filter.eq('CLOUD_COVER', 0))
# Create two composites to check the effect of filtering by CLOUD_COVER.
bad_composite = collection.mean()
good_composite = filtered.mean()
# Display the composites.
m = geemap.Map()
m.set_center(25.8544, -18.08874, 13)
m.add_layer(
bad_composite,
{'bands': ['B3', 'B2', 'B1'], 'min': 0.05, 'max': 0.35, 'gamma': 1.1},
'Bad composite',
)
m.add_layer(
good_composite,
{'bands': ['B3', 'B2', 'B1'], 'min': 0.05, 'max': 0.35, 'gamma': 1.1},
'Good composite',
)
m
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2025-07-25 UTC.
[null,null,["Ultimo aggiornamento 2025-07-25 UTC."],[[["\u003cp\u003eEarth Engine provides multiple methods for filtering image collections, including convenience functions like \u003ccode\u003efilterDate()\u003c/code\u003e and \u003ccode\u003efilterBounds()\u003c/code\u003e as well as the more general \u003ccode\u003efilter()\u003c/code\u003e method for custom filtering needs.\u003c/p\u003e\n"],["\u003cp\u003eThis example demonstrates how to filter a Landsat 8 image collection by date, month, geographic bounds, and cloud cover using these methods.\u003c/p\u003e\n"],["\u003cp\u003eFiltering by cloud cover significantly improves the quality of composites derived from image collections, as shown by comparing a composite generated from unfiltered data with one generated from data filtered for zero cloud cover.\u003c/p\u003e\n"],["\u003cp\u003eThe code example is provided in both JavaScript and Python, enabling users to apply these filtering techniques in their preferred programming environment within the Earth Engine platform.\u003c/p\u003e\n"]]],["The content demonstrates filtering image collections in Earth Engine. It uses `filterDate()`, `filterBounds()`, and `filter()` to refine a Landsat 8 dataset. The data is filtered by date (2015-2018), month (November-February), and a specific location. Further filtering removes images with high cloud cover using `CLOUD_COVER`. Two composites, one filtered for low cloud cover and one unfiltered, are then created and displayed to illustrate the effect of filtering.\n"],null,["# Filtering an ImageCollection\n\nAs illustrated in the [Get Started section](/earth-engine/guides/getstarted)\nand the [ImageCollection Information section](/earth-engine/guides/ic_info), Earth\nEngine provides a variety of convenience methods for filtering image collections.\nSpecifically, many common use cases are handled by `imageCollection.filterDate()`,\nand `imageCollection.filterBounds()`. For general purpose filtering, use\n`imageCollection.filter()` with an `ee.Filter` as an argument. The\nfollowing example demonstrates both convenience methods and `filter()`\nto identify and remove images with high cloud cover from an `ImageCollection`.\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load Landsat 8 data, filter by date, month, and bounds.\nvar collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')\n .filterDate('2015-01-01', '2018-01-01') // Three years of data\n .filter(ee.Filter.calendarRange(11, 2, 'month')) // Only Nov-Feb observations\n .filterBounds(ee.Geometry.Point(25.8544, -18.08874)); // Intersecting ROI\n\n// Also filter the collection by the CLOUD_COVER property.\nvar filtered = collection.filter(ee.Filter.eq('CLOUD_COVER', 0));\n\n// Create two composites to check the effect of filtering by CLOUD_COVER.\nvar badComposite = collection.mean();\nvar goodComposite = filtered.mean();\n\n// Display the composites.\nMap.setCenter(25.8544, -18.08874, 13);\nMap.addLayer(badComposite,\n {bands: ['B3', 'B2', 'B1'], min: 0.05, max: 0.35, gamma: 1.1},\n 'Bad composite');\nMap.addLayer(goodComposite,\n {bands: ['B3', 'B2', 'B1'], min: 0.05, max: 0.35, gamma: 1.1},\n 'Good composite');\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Load Landsat 8 data, filter by date, month, and bounds.\ncollection = (\n ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')\n # Three years of data\n .filterDate('2015-01-01', '2018-01-01')\n # Only Nov-Feb observations\n .filter(ee.Filter.calendarRange(11, 2, 'month'))\n # Intersecting ROI\n .filterBounds(ee.Geometry.Point(25.8544, -18.08874))\n)\n\n# Also filter the collection by the CLOUD_COVER property.\nfiltered = collection.filter(ee.Filter.eq('CLOUD_COVER', 0))\n\n# Create two composites to check the effect of filtering by CLOUD_COVER.\nbad_composite = collection.mean()\ngood_composite = filtered.mean()\n\n# Display the composites.\nm = geemap.Map()\nm.set_center(25.8544, -18.08874, 13)\nm.add_layer(\n bad_composite,\n {'bands': ['B3', 'B2', 'B1'], 'min': 0.05, 'max': 0.35, 'gamma': 1.1},\n 'Bad composite',\n)\nm.add_layer(\n good_composite,\n {'bands': ['B3', 'B2', 'B1'], 'min': 0.05, 'max': 0.35, 'gamma': 1.1},\n 'Good composite',\n)\nm\n```"]]
