Mukhopadhaya, S.; Kumar, A.; Stein, A. FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels. Remote Sens.2018, 10, 1707.
Mukhopadhaya, S.; Kumar, A.; Stein, A. FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels. Remote Sens. 2018, 10, 1707.
Mukhopadhaya, S.; Kumar, A.; Stein, A. FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels. Remote Sens.2018, 10, 1707.
Mukhopadhaya, S.; Kumar, A.; Stein, A. FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels. Remote Sens. 2018, 10, 1707.
Abstract
In this study, the fuzzy c- means classifier has been studied with nine other similarity and dissimilarity measures: Manhattan distance, chessboard distance, Bray-Curtis distance, Canberra, Cosine distance, correlation distance, mean absolute difference, median absolute difference 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 norms obtained were combined to study the effect of composite norms 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 norm performed better than the conventional Euclidean norm. But, due to the incapability of the FCM classifier to handle noise properly, the classification accuracy was around 75%.
Copyright:
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