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

Abstract

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%.

Editorial to Special Issue “Multispectral Image Acquisition, Processing, and Analysis”

“…The last one, "FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels" by S. Mukhopadhaya, A. Kumar, and A. Stein concerned the comparative accuracy assessment of alpha-cut embedding into fuzzy c-means classification with using different similarity and dissimilarity measures [8]. The goal was to highlight the two best measures and parameters whose combination and optimization allowed for better results to be obtained to handle the problem of mixed pixels as well as the effect of noise on the considered datasets.…”

An AHP based approach to forecast groundwater level at potential recharge zones of Uckermark District, Brandenburg, Germany

“…On the other hand, groundwater level forecasting enables us to anticipate the water quality of non-sampled depth zones and evaluate the sustainability of remaining groundwater 20 , 21 . For the last couple of decades, machine learning methods have become popular in various RS related studies 22 , 23 . The significant characteristic of artificial neural networks (ANN) is that, they are usable for solving incomplete information problems without the analytical relationship between the input and output data.…”