An unsupervised classification technique for multispectral remote sensing data.Description 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. 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. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques.
Document ID
19730055077
Acquisition Source
Legacy CDMS
Document Type
Conference Proceedings
Authors
Su, M. Y. (Northrop Services, Inc. Huntsville, Ala., United States)
Cummings, R. E. (NASA Marshall Space Flight Center Huntsville, Ala., United States)
Date Acquired
August 7, 2013
Publication Date
January 1, 1973
Subject Category
Geophysics
Meeting Information
Meeting: International Symposium on Remote Sensing of Environment