FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels

S. Mukhopadhyay (Corresponding Author), A. Kumar, A. Stein

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
91 Downloads (Pure)


In this paper, the fuzzy c-means (FCM) classifier has been studied with 12 similarity and dissimilarity measures: Manhattan distance, chessboard distance, Bray–Curtis distance, Canberra, Cosine distance, correlation distance, mean absolute difference, median absolute difference, Euclidean, Mahalanobis, diagonal Mahalanobis and normalised squared Euclidean distance. Both single and composite modes were used with a varying weight constant (m*) and also at different α-cuts. The two best single measures obtained were combined to study the effect of composite measures on the datasets used. An image-to-image accuracy check was conducted to assess the accuracy of the classified images. Fuzzy error matrix (FERM) was applied to measure the accuracy assessment outcomes for a Landsat-8 dataset with respect to the Formosat-2 dataset. To conclude, FCM classifier with Cosine measure performed better than the conventional Euclidean measure. But, due to the incapability of the FCM classifier to handle noise properly, the classification accuracy was around 75%.
Original languageEnglish
Article number1707
Pages (from-to)1-24
Number of pages24
JournalRemote sensing
Issue number11
Publication statusPublished - 1 Nov 2018




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