Page Summary
-
The SNIC algorithm performs superpixel clustering on an input image.
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The output includes a band of cluster IDs and the average values for each input band within those clusters.
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If seeds are not provided, the output will also contain a seeds band indicating the generated seed locations.
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The size and compactness parameters significantly influence the clustering results and may need adjustment based on the image.
| Usage | Returns |
|---|---|
ee.Algorithms.Image.Segmentation.SNIC(image, size
, compactness
, connectivity
, neighborhoodSize
, seeds
)
|
Image |
| Argument | Type | Details |
|---|---|---|
image
|
Image | The input image for clustering. |
size
|
Integer, default: 5 | The superpixel seed location spacing, in pixels. If 'seeds' image is provided, no grid is produced. |
compactness
|
Float, default: 1 | Compactness factor. Larger values cause clusters to be more compact (square). Setting this to 0 disables spatial distance weighting. |
connectivity
|
Integer, default: 8 | Connectivity. Either 4 or 8. |
neighborhoodSize
|
Integer, default: null | Tile neighborhood size (to avoid tile boundary artifacts). Defaults to 2 * size. |
seeds
|
Image, default: null | If provided, any non-zero valued pixels are used as seed locations. Pixels that touch (as specified by 'connectivity') are considered to belong to the same cluster. |
Examples
Code Editor (JavaScript)
// Note that the compactness and size parameters can have a significant impact // on the result. They must be adjusted to meet image-specific characteristics // and patterns, typically through trial. Pixel scale (map zoom level) is also // important to consider. When exploring interactively through map tile // visualization, the segmentation result it dependent on zoom level. If you // need to evaluate the result at a specific scale, call .reproject() on the // result, but do so with caution because it overrides the default scaling // behavior that makes tile computation fast and efficient. // Load a NAIP image for a neighborhood in Las Vegas. var naip = ee . Image ( 'USDA/NAIP/DOQQ/m_3611554_sw_11_1_20170613' ); // Apply the SNIC algorithm to the image. var snic = ee . Algorithms . Image . Segmentation . SNIC ({ image : naip , size : 30 , compactness : 0.1 , connectivity : 8 , }); // Display the original NAIP image as RGB. // Lock map zoom to maintain the desired scale of the segmentation computation. Map . setLocked ( false , 18 , 18 ); Map . setCenter ( - 115.32053 , 36.182016 , 18 ); Map . addLayer ( naip , null , 'NAIP RGB' ); // Display the clusters. Map . addLayer ( snic . randomVisualizer (), null , 'Clusters' ); // Display the RGB cluster means. var visParams = { bands : [ 'R_mean' , 'G_mean' , 'B_mean' ], min : 0 , max : 255 }; Map . addLayer ( snic , visParams , 'RGB cluster means' );
import ee import geemap.core as geemap
Colab (Python)
# Note that the compactness and size parameters can have a significant impact # on the result. They must be adjusted to meet image-specific characteristics # and patterns, typically through trial. Pixel scale (map zoom level) is also # important to consider. When exploring interactively through map tile # visualization, the segmentation result it dependent on zoom level. If you # need to evaluate the result at a specific scale, call .reproject() on the # result, but do so with caution because it overrides the default scaling # behavior that makes tile computation fast and efficient. # Load a NAIP image for a neighborhood in Las Vegas. naip = ee . Image ( 'USDA/NAIP/DOQQ/m_3611554_sw_11_1_20170613' ) # Apply the SNIC algorithm to the image. snic = ee . Algorithms . Image . Segmentation . SNIC ( image = naip , size = 30 , compactness = 0.1 , connectivity = 8 ) # Display the original NAIP image as RGB. m = geemap . Map () m . set_center ( - 115.32053 , 36.182016 , 18 ) m . add_layer ( naip , None , 'NAIP RGB' ) # Display the clusters. m . add_layer ( snic . randomVisualizer (), None , 'Clusters' ) # Display the RGB cluster means. vis_params = { 'bands' : [ 'R_mean' , 'G_mean' , 'B_mean' ], 'min' : 0 , 'max' : 255 } m . add_layer ( snic , vis_params , 'RGB cluster means' ) m
