Semi-supervised fuzzy C-means clustering for change detection from multispectral satellite image

Data clustering has been applied in almost areas such as health, natural resource management, urban planning∶ especially, fuzzy clustering which the advantage with handling better for ambiguous data. This paper proposes a method of improving fuzzy c-means clustering algorithm by using the criteria to move the prototype of clusters to the expected centroids which are pre-determined on the basis of samples. The proposed algorithm is used for a model of change detection on multispectral satellite imagery at multiple temporals. The experiments are implemented on various data sets in comparison with other approaches.