Unsupervised Change Detection of Remotely Sensed Images Using Fuzzy Clustering

Abstract

In this paper two fuzzy clustering algorithms, namely Fuzzy C-Means (FCM) and Gustafson Kessel Clustering (GKC), have been used for detecting changes in multitemporal remote sensing images. Change detection maps are obtained by separating the pixel-patterns of the difference image into two groups. To show the effectiveness of the proposed technique, experiments are conducted on three multispectral and multitemporal images. Results are compared with those of existing Marko Random Field (MRF) & neural network based algorithms and found to be superior. The proposed technique is less time-consuming and unlike MRF do not need any a priori knowledge of distribution of changed and unchanged pixels (as required by MRF).