A non-parametric clustering scheme for landsat

摘要

A 4-dimensional histogram is computed to reduce the large LANDSAT pixel data (up to 7.6 million pixels to a frame) to the much smaller number (6,000) of distinct vectors and their frequency of occurrence in the scene. The vectors are clustered by a recent non-parametric clustering algorithm(3) using the histogram count as a probability density estimate. The resultant clusters are unimodal m the 4-dimensional histogram and can possess arbitrary shapes. The algorithm is non-iterative and does not require specification of the number of clusters a priori.Hashing is used to generate the histogram and also subsequent table look-up classification of the individual pixels in the image after the histogram vectors are clustered. The resultant clustering scheme is very efficient and a 512 × 512 LANDSAT scene can be clustered in less than 2 min of CPU time on a PDP-10 computer. Results of the application of the clustering scheme on representative LANDSAT scenes are included.