Histogram based clutter identification.

This identification algorithm uses the histogram of temporal accumulated rainfall. It iteratively detects classes whose frequency falls below a specified percentage (1% by default) of the frequency of the class with the biggest frequency and remove the values from the dataset until the changes from iteration to iteration falls below a threshold. This algorithm is able to detect static clutter as well as shadings. It is suggested to choose a representative time periode for the input precipitation accumulation. The recommended time period should cover one year.


prec_accum (numpy.ndarray) – spatial array containing rain accumulation


output (numpy.ndarray) – boolean array with pixels identified as clutter/shadings set to True.


See Heuristic clutter detection based on distribution properties (“histo cut”).