On Fuzzy Thresholding of Remotely Sensed Images
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.
KeywordsMembership Function Image Segmentation Gray Level Conditional Entropy Fuzzy Entropy
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