On Fuzzy Thresholding of Remotely Sensed Images

  • B. Uma Shankar
  • A. Ghosh
  • S. K. Pal
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 42)


Effectiveness of various fuzzy thresholding techniques (based on entropy of fuzzy sets, fuzzy geometrical properties, and fuzzy correlation) is demonstrated on remotely sensed images. A new quantitative index for image segmentation using the concept of homogeneity within regions is defined. Results are compared with those of probabilistic thresholding, and fuzzy c-means and hard c-means cluster­ing algorithms, both in terms of index value (quantitatively) and structural de­tails (qualitatively). Fuzzy set theoretic algorithms are seen to be superior to their respective non-fuzzy counter parts. Among all the techniques fuzzy correlation, followed by fuzzy entropy, performed better for extracting the structures. Fuzzy geometry based thresholding algorithms produced a single stable threshold for a wide range of membership variation. Both IRS and SPOT imagery are considered for this investigation.


Membership Function Image Segmentation Gray Level Conditional Entropy Fuzzy Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • B. Uma Shankar
    • 1
  • A. Ghosh
    • 1
  • S. K. Pal
    • 1
  1. 1.Machine Intelligence UnitIndian Statistical InstituteCalcuttaIndia

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