AI-generated Key Takeaways
-
The
toArray()method concatenates pixels from each band into a single array per pixel. -
The result of
toArray()will be masked if any input bands are masked. -
The
axisargument determines the axis along which concatenation occurs, affecting the dimensionality of the output array.
| Usage | Returns |
|---|---|
Image.
toArray
( axis
)
|
Image |
| Argument | Type | Details |
|---|---|---|
|
this:
image
|
Image | Image of bands to convert to an array per pixel. Bands must have scalar pixels, or array pixels with equal dimensionality. |
axis
|
Integer, default: 0 | Axis to concatenate along; must be at least 0 and at most the dimension of the inputs. If the axis equals the dimension of the inputs, the result will have 1 more dimension than the inputs. |
Examples
Code Editor (JavaScript)
// A function to print arrays for a selected pixel in the following examples. function sampArrImg ( arrImg ) { var point = ee . Geometry . Point ([ - 121 , 42 ]); return arrImg . sample ( point , 500 ). first (). get ( 'array' ); } // A 3-band image of constants. var img = ee . Image ([ 0 , 1 , 2 ]); print ( '3-band image' , img ); // Convert the 3-band image to an array image. The resulting array image has a // single band named "array". The "array" band stores the per-pixel band values // from the input ee.Image as a 1D array. var arrayImg1D = img . toArray (); print ( '1D array image' , arrayImg1D ); // Sample a single pixel to see its 1D array using the `sampArrImg` function // defined above. Similar arrays are present for all pixels in a given array // image; looking at just one helps conceptualize the structure. print ( '1D array image (pixel)' , sampArrImg ( arrayImg1D )); // [0, 1, 2] // Array images can be displayed to the Code Editor map and queried with the // inspector tool. Per-pixel, the first element in the array is shown. // Single-band grayscale is used to represent the data. Style parameters `min` // and `max` are valid. Styling the layer with the Code Editor's layer // visualization tool is invalid. Map . addLayer ( arrayImg1D , { min : 0 , max : 2 }, 'Image array' ); // Create a 2D array image by concatenating the values in a 1D array image // along the 1-axis using `toArray(1)`. For a 3D array, apply `toArray(2)` to // the result. var arrayImg2D = arrayImg1D . toArray ( 1 ); print ( '2D array image (pixel)' , sampArrImg ( arrayImg2D )); // [[0], // [1], // [2]]
import ee import geemap.core as geemap
Colab (Python)
# A function to print arrays for a selected pixel in the following examples. def samp_arr_img ( arr_img ): point = ee . Geometry . Point ([ - 121 , 42 ]) return arr_img . sample ( point , 500 ) . first () . get ( 'array' ) # A 3-band image of constants. img = ee . Image ([ 0 , 1 , 2 ]) display ( '3-band image' , img ) # Convert the 3-band image to an array image. The resulting array image has a # single band named "array". The "array" band stores the per-pixel band values # from the input ee.Image as a 1D array. array_img_1_d = img . toArray () display ( '1D array image' , array_img_1_d ) # Sample a single pixel to see its 1D array using the `samp_arr_img` function # defined above. Similar arrays are present for all pixels in a given array # image looking at just one helps conceptualize the structure. display ( '1D array image (pixel)' , samp_arr_img ( array_img_1_d )) # [0, 1, 2] # Array images can be displayed to the Code Editor map and queried with the # inspector tool. Per-pixel, the first element in the array is shown. # Single-band grayscale is used to represent the data. Style parameters `min` # and `max` are valid. Styling the layer with the Code Editor's layer # visualization tool is invalid. m = geemap . Map () m . add_layer ( array_img_1_d , { 'min' : 0 , 'max' : 2 }, 'Image array' ) display ( m ) # Create a 2D array image by concatenating the values in a 1D array image # along the 1-axis using `toArray(1)`. For a 3D array, apply `toArray(2)` to # the result. array_img_2_d = array_img_1_d . toArray ( 1 ) display ( '2D array image (pixel)' , samp_arr_img ( array_img_2_d )) # [[0], # [1], # [2]]
