Abstract
For improving image attributes and increase robustness to handle the noise for image classification, fuzzy algorithms were introduced. The fuzzy c-means (FCM) algorithm has not been suggested to incorporate spectral and spatial neighborhood information for accomplishing image classification. In this study, evolution developed for overall accuracy (OA) calculated by FERM (fuzzy error matrix) technique for an algorithm, namely fuzzy local information c-means (FLICM) using base classifier FCM, adaptive fuzzy local information c-means (ADFLICM) using base classifier FCM and fuzzy c-means with constraints. For these algorithms, various distance measures and weighting components (m) are studied. This study provides a comparison among different forms of local convolution with an FCM classifier, with a combination of different distance measures and weighting components to select the best classification approach. FLICM using base classifier FCM and Euclidean distance measures with a weighting component (m) of 1.3 has resulted in the highest overall accuracy compared to other classifiers used with various distance measures.
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Acknowledgements
The authors would like to express their sincere gratitude to Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad for their help and support, and also to Indian Institute of Remote Sensing Dehradun for their kind co-operation during the course of this study.
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Suman, S., Kumar, D. & Kumar, A. Study the Effect of Convolutional Local Information-Based Fuzzy c-Means Classifiers with Different Distance Measures. J Indian Soc Remote Sens 49, 1561–1568 (2021). https://doi.org/10.1007/s12524-021-01333-6
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DOI: https://doi.org/10.1007/s12524-021-01333-6