Introduction
For more information, please read the block description.
Block type: PROCESSING
This block provides a k-means clustering algorithm. It creates a given number of clusters in an image and assigns each pixel to a cluster. Nodata pixels in the input dataset are not 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.
This block can only process images with a maximum size of 1.2GB, because the entire image is loaded at once into memory. Any image with a larger size will generate an error.
Supported parameters
n_clusters
: The number of clusters for the K-means clustering. Number of classes in the output file. Default is 6.n_iterations
: The number of iterations for the K-means clustering. Default is 10.n_sieve_pixels
: Minimum number of pixels in each patch or group for the classification. Default is 64.
Example parameters using the data block SPOT Display (Streaming) and the processing block K-Means Clustering:
{
"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
}
}
Output format
Output and input format are identical. All metadata elements provided by the input dataset as properties are propagated to the output.