ImageCollection को फ़िल्टर करना
संग्रह की मदद से व्यवस्थित रहें
अपनी प्राथमिकताओं के आधार पर, कॉन्टेंट को सेव करें और कैटगरी में बांटें.
शुरू करें सेक्शन और इमेज कलेक्शन की जानकारी वाले सेक्शन में दिखाए गए तरीके के मुताबिक, Earth Engine में इमेज कलेक्शन को फ़िल्टर करने के कई तरीके उपलब्ध हैं.
खास तौर पर, इस्तेमाल के कई सामान्य उदाहरणों को imageCollection.filterDate() और imageCollection.filterBounds() मैनेज करते हैं. सामान्य तौर पर फ़िल्टर करने के लिए, आर्ग्युमेंट के तौर पर ee.Filter के साथ
imageCollection.filter() का इस्तेमाल करें. यहां दिए गए उदाहरण में, ImageCollection से ज़्यादा बादल वाली इमेज की पहचान करने और उन्हें हटाने के लिए, आसानी से इस्तेमाल किए जा सकने वाले दोनों तरीके और filter() के बारे में बताया गया है.
कोड एडिटर (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');
Python सेटअप
Python API के बारे में जानकारी पाने और इंटरैक्टिव डेवलपमेंट के लिए geemap का इस्तेमाल करने के लिए,
Python एनवायरमेंट पेज देखें.
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
जब तक कुछ अलग से न बताया जाए, तब तक इस पेज की सामग्री को Creative Commons Attribution 4.0 License के तहत और कोड के नमूनों को Apache 2.0 License के तहत लाइसेंस मिला है. ज़्यादा जानकारी के लिए, Google Developers साइट नीतियां देखें. Oracle और/या इससे जुड़ी हुई कंपनियों का, Java एक रजिस्टर किया हुआ ट्रेडमार्क है.
आखिरी बार 2025-07-25 (UTC) को अपडेट किया गया.
[null,null,["आखिरी बार 2025-07-25 (UTC) को अपडेट किया गया."],[[["\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```"]]
