Applies the given reducer to the neighborhood around each pixel, as determined by the given kernel. If the reducer has a single input, it will be applied separately to each band of the collection; otherwise it must have the same number of inputs as the input image has bands.

The reducer output names determine the names of the output bands: reducers with multiple inputs will use the output names directly, while reducers with a single input will prefix the output name with the input band name (e.g. '10_mean', '20_mean', etc.).

Reducers with weighted inputs can have the input weight based on the input mask, the kernel value, or the smaller of those two.

Image.reduceNeighborhood(reducer, kernel, inputWeight, skipMasked, optimization)Image
this: imageImageThe input image.
reducerReducerThe reducer to apply to pixels within the neighborhood.
kernelKernelThe kernel defining the neighborhood.
inputWeightString, default: "kernel"One of 'mask', 'kernel', or 'min'.
skipMaskedBoolean, default: trueMask output pixels if the corresponding input pixel is masked.
optimizationString, default: nullOptimization strategy. Options are 'boxcar' and 'window'. The 'boxcar' method is a fast method for computing count, sum or mean. It requires a homogeneous kernel, a single-input reducer and either MASK, KERNEL or no weighting. The 'window' method uses a running window, and has the same requirements as 'boxcar', but can use any single input reducer. Both methods require considerable additional memory.