Review of Fuzzy Soft Classification with Contextual Information

As real world changes very rapidly, it is creating a problem of mixed pixels and the pixels belonging to one class are moving to the other class according to their spatial resolution. Therefore, uncertainty increases at the boundary locations. Fuzzy C-mean, Possibilistic C-mean, and Noise clustering soft classification techniques used to classify mixed pixels on the basis of spectral information. There are some models as like Smoothness Prior and Discontinuity Adaptive prior based on spatial information, are available to classify the pixels with minimum uncertainty. Spatial information of pixel gives the contextual information of pixel, which is based on the neighborhood pixel. With the help of neighboring pixels, Smoothness prior smoothen the pixels and preserve the edges. Whereas Discontinuity Adaptive Prior smoothen the pixels and overcome the problem of over smoothness. In this paper, we are going to review the Fuzzy C mean, Possibilistic C mean, and Noise Clustering with Smoothness Prior and Discontinuity Adaptive Prior.