Classification of Landsat remote sensing images by a fuzzy unsupervised clustering algorithm

Frank Y. Shih, Gwotsong P. Chen

Research output: Contribution to conferencePaperpeer-review

Abstract

Due to the low resolution of Landsat images and the multiplicity of the terrain, it is improper to classify each pixel in a Landsat image to one of land cover types by using the conventional remote sensing classification methods. The concept of fuzzy sets provides a flexible approach to resolve this problem. This paper presents a new two-pass mode unsupervised clustering algorithm incorporated with the underlying fuzzy theory. In the first pass the mean vectors representing the geographic attributes or the land cover types are derived. The second pass is based on the fuzzy theory. That is the mean vectors obtained are used to generate a membership function. The grade of memberships for each pixel with respect to various land cover types is computed according to the distance from the pixel to all the clusters' mean vector.

Original languageEnglish (US)
Pages314
Number of pages1
StatePublished - 1992
EventFirst International Conference on Fuzzy Theory and Technology Proceedings, Abstracts and Summaries -
Duration: Oct 14 1992Oct 18 1992

Other

OtherFirst International Conference on Fuzzy Theory and Technology Proceedings, Abstracts and Summaries
Period10/14/9210/18/92

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software
  • Computational Theory and Mathematics
  • Artificial Intelligence

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