Document Type


Date of Award


Degree Name

Master of Science in Computer Science - (M.S.)


Computer and Information Science

First Advisor

Frank Y. Shih


Due to the resolution of Landsat images and the multiplicity of the terrain, it is improper to assign each pixel in an image to one of a number of land cover types by using the conventional remote sensing classification method. This is also known as the hard partition method. The concept of the fuzzy set provides the means to resolve this problem. This paper presents a two-pass-mode fuzzy unsupervised clustering algorithm.

In the first passing, the cluster mean vectors which represent the geographic attributes or the land cover types are derived. In the second passing, the concept of fuzzy set is used. The cluster mean vectors which are obtained in the first passing are used to derive the membership function. The grade of memberships of each pixel to the land cover types are obtained according to the distance from the pixel to each cluster mean vector. The output of this algorithm can be used as the input of the Geographic Information System.



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