Segmentation of a thematic mapper image using the fuzzy c-means clustering algorithm

In this paper, a segmentation procedure that utilizes a clustering algorithm based upon fuzzy set theory is developed. The procedure operates in a nonparametric unsupervised mode. The feasibility of the methodology is demonstrated by segmenting a six-band Landsat-4 digital image with 324 scan lines...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 1986-05, Vol.24 (3), p.400-408
Main Authors: CANNON, R. L, DAVE, J. V, BEZDEK, J. C, TRIVEDI, M. M
Format: Article
Language:English
Subjects:
Agronomy. Soil science and plant productions
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
Generalities
Generalities. Biometrics, experimentation. Remote sensing
Remote sensing
Soil science
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Summary:In this paper, a segmentation procedure that utilizes a clustering algorithm based upon fuzzy set theory is developed. The procedure operates in a nonparametric unsupervised mode. The feasibility of the methodology is demonstrated by segmenting a six-band Landsat-4 digital image with 324 scan lines and 392 pixels per scan line. The segmentation method uses the fuzzy c-means algorithm in two stages. The large number of clusters resulting from this segmentation process are then merged by use of a similiarity measure on the cluster centers.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.1986.289598