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提供意見
ee.ImageCollection.getRegion
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
針對 ImageCollection 中的每個 [像素、波段、圖片] 元組,輸出值陣列。輸出內容包含 ID、經度、緯度、時間和所有波段的資料列,這些資料列對應於指定區域中與每個像素相交的每張圖片。如果嘗試擷取超過 1048576 個值,就會發生錯誤。
用量 傳回 ImageCollection. getRegion (geometry, scale , crs , crsTransform )
清單
引數 類型 詳細資料 這個:collection
ImageCollection 要從中擷取資料的圖片集。 geometry
幾何圖形 要擷取資料的區域。 scale
浮點值,預設值為空值 以公尺為單位的投影名義比例,用於工作。 crs
投影 (選用) 要使用的投影機。如未指定,則預設為 EPSG:4326。如果除了比例之外還指定了投影,系統會將投影重新調整為指定比例。 crsTransform
清單,預設值為空值 CRS 轉換值的陣列。這是 3x2 仿射轉換的列優先順序。這個選項與縮放選項互斥,且會取代指定投影中已設定的任何變形。
範例
程式碼編輯器 (JavaScript)
// A Landsat 8 TOA image collection (3 months at a specific point, RGB bands).
var col = ee . ImageCollection ( 'LANDSAT/LC08/C02/T1_TOA' )
. filterBounds ( ee . Geometry . Point ( - 90.70 , 34.71 ))
. filterDate ( '2020-07-01' , '2020-10-01' )
. select ( 'B[2-4]' );
print ( 'Collection' , col );
// Define a region to get pixel values for. This is a small rectangle region
// that intersects 2 image pixels at 30-meter scale.
var roi = ee . Geometry . BBox ( - 90.496353 , 34.851971 , - 90.495749 , 34.852197 );
// Display the region of interest overlaid on an image representative. Note
// the ROI intersection with 2 pixels.
var visParams = {
bands : [ 'B4' , 'B3' , 'B2' ],
min : 0.128 ,
max : 0.163
};
Map . setCenter ( - 90.49605 , 34.85211 , 19 );
Map . addLayer ( col . first (), visParams , 'Image representative' );
Map . addLayer ( roi , { color : 'white' }, 'ROI' );
// Fetch pixel-level information from all images in the collection for the
// pixels intersecting the ROI.
var pixelInfoBbox = col . getRegion ({
geometry : roi ,
scale : 30
});
// The result is a table (a list of lists) where the first row is column
// labels and subsequent rows are image pixels. Columns contain values for
// the image ID ('system:index'), pixel longitude and latitude, image
// observation time ('system:time_start'), and bands. In this example, note
// that there are 5 images and the region intersects 2 pixels, so n rows
// equals 11 (5 * 2 + 1). All collection images must have the same number of
// bands with the same names.
print ( 'Extracted pixel info' , pixelInfoBbox );
// The function accepts all geometry types (e.g., points, lines, polygons).
// Here, a multi-point geometry with two points is used.
var points = ee . Geometry . MultiPoint ([[ - 90.49 , 34.85 ], [ - 90.48 , 34.84 ]]);
var pixelInfoPoints = col . getRegion ({
geometry : points ,
scale : 30
});
print ( 'Point geometry example' , pixelInfoPoints );
Python 設定
請參閱
Python 環境 頁面,瞭解 Python API 和如何使用 geemap
進行互動式開發。
import ee
import geemap.core as geemap
Colab (Python)
# A Landsat 8 TOA image collection (3 months at a specific point, RGB bands).
col = (
ee . ImageCollection ( 'LANDSAT/LC08/C02/T1_TOA' )
. filterBounds ( ee . Geometry . Point ( - 90.70 , 34.71 ))
. filterDate ( '2020-07-01' , '2020-10-01' )
. select ( 'B[2-4]' )
)
display ( 'Collection' , col )
# Define a region to get pixel values for. This is a small rectangle region
# that intersects 2 image pixels at 30-meter scale.
roi = ee . Geometry . BBox ( - 90.496353 , 34.851971 , - 90.495749 , 34.852197 )
# Display the region of interest overlaid on an image representative. Note
# the ROI intersection with 2 pixels.
vis_params = { 'bands' : [ 'B4' , 'B3' , 'B2' ], 'min' : 0.128 , 'max' : 0.163 }
m = geemap . Map ()
m . set_center ( - 90.49605 , 34.85211 , 19 )
m . add_layer ( col . first (), vis_params , 'Image representative' )
m . add_layer ( roi , { 'color' : 'white' }, 'ROI' )
display ( m )
# Fetch pixel-level information from all images in the collection for the
# pixels intersecting the ROI.
pixel_info_bbox = col . getRegion ( geometry = roi , scale = 30 )
# The result is a table (a list of lists) where the first row is column
# labels and subsequent rows are image pixels. Columns contain values for
# the image ID ('system:index'), pixel longitude and latitude, image
# observation time ('system:time_start'), and bands. In this example, note
# that there are 5 images and the region intersects 2 pixels, so n rows
# equals 11 (5 * 2 + 1). All collection images must have the same number of
# bands with the same names.
display ( 'Extracted pixel info' , pixel_info_bbox )
# The function accepts all geometry types (e.g., points, lines, polygons).
# Here, a multi-point geometry with two points is used.
points = ee . Geometry . MultiPoint ([[ - 90.49 , 34.85 ], [ - 90.48 , 34.84 ]])
pixel_info_points = col . getRegion ( geometry = points , scale = 30 )
display ( 'Point geometry example' , pixel_info_points )
提供意見
除非另有註明,否則本頁面中的內容是採用創用 CC 姓名標示 4.0 授權 ,程式碼範例則為阿帕契 2.0 授權 。詳情請參閱《Google Developers 網站政策 》。Java 是 Oracle 和/或其關聯企業的註冊商標。
上次更新時間:2025-07-26 (世界標準時間)。
想進一步說明嗎?
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間:2025-07-26 (世界標準時間)。"],[],["The `ImageCollection.getRegion` method extracts pixel values from an ImageCollection within a specified geometry. It returns a list containing rows of data for each \\[pixel, band, image\\] tuple, including id, longitude, latitude, time, and band values. Users define the extraction region, scale, and optionally the projection. The output format is a table where rows represent pixels and columns detail each image's data. The method accepts various geometry types but is limited to extracting 1,048,576 values per request.\n"]]