अमूर्त

Unsupervised Change Detection for Satellite Images using Normalized Neighborhood Ratio and Gustafson KesselClustering

Krishna Kant Singh, Neelima Saini, Nitin Garg, Sunita Mandal, Nitigya Grover

This paper presents an unsupervised change detection method for satellite images based on normalized neighborhood Gustafson Kessel Clustering. The method works in two phases in the first phase a difference image is created from the two bi temporal satellite images using normalized neighborhood ratio. The difference image is then clustered into two clusters change and unchanged using Gustafson Kessel Clustering to create the change map. In the change map the changed areas are highlighted. The method is applied on bi-temporal images of Reno lake Tahoe area and the results obtained from the proposed method are compared with some other state of the art methods. The quantative as well as qualitative comparison of the results show that the proposed method gives much more accurate results than the other existing methods.