Clustering the Vegetation Areas using Fuzzy CMeans Algorithm

To detect the vegetation land from google earth image and clustered that vegetation land to get the different clusters and so the area of clustered land is calculated. The detection is done by land cover classification using a fuzzy  C means clustering because it overcomes the disadvantage of K means clustering algorithm because that clustered land is based on the land attributes not a particular distance. The exhibition of the FCM algorithm relies upon the choice of the primary cluster focus and the primary enrollment esteem. On the off chance that best primary cluster focus that is near the real, last cluster focus can be discovered, the FCM algorithm quickly cover the particular area and the preparing time can be radically decreased. Which altogether diminishes the calculation time required to segment a dataset into desired clusters?