Remote Sensing Classification Using Fuzzy C-means Clustering with Spatial Constraints Based on Markov Random Field

Abstract

This paper proposes a new clustering algorithm which integrates Fuzzy C-means clustering with Markov random field (FCM). The density function of the first principal component which sufficiently reflects the class differences and is applied in determining of initial labels for FCM algorithm. Thus, the sensitivity to the random initial values can be avoided. Meanwhile, this algorithm takes into account the spatial correlation information of pixels. The experiments on the synthetic and QuickBird images show that the proposed method can achieve better classification accuracy and visual qualities than the general FCM algorithm.

Keyword:

• Fuzzy c-means clustering
• Markov random field
• remote sensing classification
• Kernel density function