![]() ![]() This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. It consists of two parts: (a) a sequential statistical clustering which is essentially a sequential variance analysis and (b) a generalized K-means clustering. ![]() E.Ī new clustering technique is presented. ![]() Unsupervised classification of earth resources data. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques. ![]() An unsupervised classification technique for multispectral remote sensing data.ĭescription of a two-part clustering technique consisting of (a) a sequential statistical clustering, which is essentially a sequential variance analysis, and (b) a generalized K-means clustering. ![]()
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