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ABSTRACT

The iterative guided spectral class rejection (IGSCR) classification algorithm uses an underlying clustering method and a decision rule to arrive at final classifications for remotely sensed data. Previous versions of IGSCR have used a hard clustering method such as k-means or ISODATA. In an effort to ultimately create a fuzzy version of IGSCR, this work uses an underlying fuzzy clustering algorithm within the IGSCR framework to study the effects of using the fuzzy clustering algorithm. IGSCR with fuzzy k-means was applied to a Landsat ETM+ satellite image to produce a two class classification (forest and nonforest), and results show that although fuzzy k-means did not lead to increased accuracy, the classification results are dramatically different for IGSCR using traditional k-means and fuzzy k-means.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Uncertainty, "fuzzy," and probabilistic reasoning

General Terms:
Algorithms, Design, Performance