A feature reduction and unsupervised classification algorithm for multispectral data

摘要

A new scheme, incorporating dimensionality reduction and clustering, suitable for classification of a large volume of remotely sensed data using a small amount of memory is proposed. The scheme involves transforming the data from multidimensional n-space to a 3-dimensional primary color space of blue, green and red coordinates. The dimensionality reduction is followed by data reduction, which involves assigning 3-dimensional samples to a 2-dimensional array. Finally, a multi-stage ISODATA technique incorporating a novel seedpoint picking method is used to obtain the desired number of clusters.The storage requirements are reduced to a low value by making five passes through the data and storing necessary information during each pass. The first three passes are used to find the minimum and maximum values of some of the variables. The data reduction is done and a classification table is formed during the fourth pass. The classification map is obtained during the fifth pass. The computer memory required is about 2K machine words.The efficacy of the algorithm is justified by simulation studies using multispectral LANDSAT data.