Description
A processing block that creates a given number of clusters in an image and assigns each pixel to a cluster.
See this block on the marketplace.
How it works
This block can only process images with a maximum size of 1.2 GB, because the entire image is loaded at once into memory. Any image with a larger size will generate an error.
Nodata pixels in the input dataset aren't excluded during the clustering. If the nodata value has a large distance from all other pixels (measured by the Frobenius or L2 norm), all nodata pixels will likely form a separate cluster.
| Parameter | Overview |
|---|---|
n_clusters | integer / required The number of clusters for the K-means clustering and the number of classes in the output file. The default value is 6. |
n_iterations | integer / required The number of iterations for the K-means clustering. The default value is 10. |
n_sieve_pixels | integer / required The minimum number of pixels in each patch or group for the classification. The default value is 64. |
Example
A workflow with SPOT 6/7 Display (Streaming).
{
"oneatlas-spot-aoiclipped:1": {
"bbox": null,
"time": null,
"limit": 3,
"intersects": {
"type": "Polygon",
"coordinates": [
[
[18.42631, -33.912732],
[18.420799, -33.922741],
[18.44355, -33.924784],
[18.441069, -33.913781],
[18.42631, -33.912732]
]
]
},
"zoom_level": 17
},
"k-means-clustering:1": {
"n_clusters": 6,
"n_iterations": 10,
"n_sieve_pixels": 64
}
}Capabilities
Input
Output
To know more please check the block capabilities specifications.