A generalized histogram clustering scheme for multidimensional image data

摘要

A clustering procedure called HICAP (HIstogram Cluster Analysis Procedure) was developed to perform an unsupervised classification of multidimensional image data. The clustering approach used in HICAP is based upon an algorithm described by Narendra and Goldberg to classify four-dimensional Landsat Multispectral Scanner data. HICAP incorporates two major modifications to the scheme by Narendra and Goldberg. The first modification is that HICAP is generalized to process up to 32-bit data with an arbitrary number of dimensions. The second modification is that HICAP uses more efficient algorithms to implement the clustering approach described by Narendra and Goldberg.(1) This means that the HICAP classification requires less computation, although it is otherwise identical to the original classification. The computational savings afforded by HICAP increases with the number of dimensions in the data.