ee.Image.remap

從輸入值對應至輸出值，以兩個平行清單表示。如果輸入清單中未包含任何輸入值，系統會將這些值設為 defaultValue (如有提供)，否則會遮蓋這些值。請注意，如果輸入值包含浮點值，有時可能會因浮點精確度錯誤而無法比對。

用量	傳回
Image.remap(from, to, defaultValue, bandName)	圖片

引數	類型	詳細資料
這個：image	圖片	要套用重新對應的圖片。
from	清單	來源值 (數字或 ee.Array)。這個清單中的所有值都會對應至「to」中的相應值。
to	清單	目的地值 (數字或 ee.Array)。這些值會用於取代「from」中的對應值。必須與「from」的值數量相同。
defaultValue	物件，預設值：null	預設值，用於取代「from」中沒有相符的值。如未指定，系統會遮蓋不相符的值。
bandName	字串，預設值為空值	要重新對應的頻帶名稱。如未指定，系統會使用圖片中的第一個波段。

範例

程式碼編輯器 (JavaScript)

// A land cover image.
var img = ee.Image('ESA/WorldCover/v100/2020');

// A list of pixel values to replace.
var fromList = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100];

// A corresponding list of replacement values (10 becomes 1, 20 becomes 2, etc).
var toList =   [ 1,  2,  2,  2,  3,  2,  4,  5,  6,  6,  2];

// Replace pixel values in the image. If the image is multi-band, only the
// remapped band will be returned. The returned band name is "remapped".
// Input image properties are retained in the output image.
var imgRemap = img.remap({
  from: fromList,
  to: toList,
  defaultValue: 0,
  bandName: 'Map'
});

// Display the original and remapped images. Note that similar land cover
// classes in the original image are grouped into aggregate classes by
// from → to value mapping.
Map.addLayer(img, null, 'Original image');
Map.addLayer(imgRemap, {
    min: 1, max: 6,
    palette:'darkgreen, lightgreen, red, white, blue, lightblue'
  }, 'Remapped image');

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 land cover image.
img = ee.Image('ESA/WorldCover/v100/2020')

# A list of pixel values to replace.
from_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100]

# A corresponding list of replacement values (10 becomes 1, 20 becomes 2, etc).
to_list = [1, 2, 2, 2, 3, 2, 4, 5, 6, 6, 2]

# Replace pixel values in the image. If the image is multi-band, only the
# remapped band will be returned. The returned band name is "remapped".
# Input image properties are retained in the output image.
img_remap = img.remap(from_list, to_list, defaultValue=0, bandName='Map')

# Display the original and remapped images. Note that similar land cover
# classes in the original image are grouped into aggregate classes by
# from → to value mapping.
m = geemap.Map()
m.add_layer(img, None, 'Original image')
m.add_layer(
    img_remap,
    {
        'min': 1,
        'max': 6,
        'palette': [
            'darkgreen',
            'lightgreen',
            'red',
            'white',
            'blue',
            'lightblue',
        ],
    },
    'Remapped image',
)
m