ee.Algorithms.Image.Segmentation.KMeans

AI-generated Key Takeaways

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• Performs K-Means clustering on an input image, assigning each pixel to a cluster and outputting a single-band image with cluster IDs.

• Offers flexibility to control clustering with parameters like numClusters, numIterations, neighborhood size, and grid size.

• neighborhoodSize dictates the tile overlap for cluster computation, while gridSize allows independent clustering within fixed-size cells.

• Setting uniqueLabels to true ensures distinct cluster IDs across the entire image, while false results in repeated IDs per tile or grid cell.

• By default, uses tiles without overlap for clustering and assigns unique labels to clusters.

Performs K-Means clustering on the input image. Outputs a 1-band image containing the ID of the cluster that each pixel belongs to. The algorithm can work either on a fixed grid of non-overlapping cells (gridSize, which can be smaller than a tile) or on tiles with overlap (neighborhoodSize). The default is to use tiles with no overlap. Clusters in one cell or tile are unrelated to clusters in another. Any cluster that spans a cell or tile boundary may receive two different labels in the two halves. Any input pixels with partial masks are fully masked in the output.

Usage	Returns
ee.Algorithms.Image.Segmentation.KMeans(image, numClusters, numIterations, neighborhoodSize, gridSize, forceConvergence, uniqueLabels)	Image

Argument	Type	Details
image	Image	The input image for clustering.
numClusters	Integer, default: 8	Number of clusters.
numIterations	Integer, default: 20	Number of iterations.
neighborhoodSize	Integer, default: 0	Neighborhood size. The amount to extend each tile (overlap) when computing the clusters. This option is mutually exclusive with gridSize.
gridSize	Integer, default: null	Grid cell-size. If greater than 0, kMeans will be run independently on cells of this size. This has the effect of limiting the size of any cluster to be gridSize or smaller. This option is mutually exclusive with neighborhoodSize.
forceConvergence	Boolean, default: false	If true, an error is thrown if convergence is not achieved before numIterations.
uniqueLabels	Boolean, default: true	If true, clusters are assigned unique IDs. Otherwise, they repeat per tile or grid cell.