Classification of satellite images using new Fuzzy cluster centroid for unsupervised classification algorithm

Vagueness in the boundaries of land cover classes is one of the important problems in the image classification. Fuzzy c means (FCM) is a traditional clustering algorithm that has been widely used in the satellite image classification. However, this algorithm has the drawback of falling into a local minimum and it needs much time to accomplish the classification for a large data set. In order to overcome these drawbacks, a New Fuzzy Cluster Centroid (NFCC) for unsupervised classification algorithm is proposed to improve the traditional FCM and fuzzy weighted c means (FWCM) algorithm. In this work a, new objective function is formulated by adding the new term along with the distance between the pixels and cluster centers in the spectral domain. This new term is formulated by multiplying the Lagrange's multiplier with the membership values of the pixel for a particular class is subtracted with one. It gives weightage to the instance of a particular pixel. The inclusion of the fuzzy centroid for each cluster increases the stability of the algorithm and the inclusion of the new term reduces the number of iterations for image classification. The technique was applied to both IKONOS and QuickBird images. Overall accuracy statistics indicates that NFCC for unsupervised classification algorithm increases the accuracy of the satellite image classification at the pixel level.