Semi-supervised Fuzzy Clustering Algorithms for Change Detection in Remote Sensing Images

Abstract

For the problem of change detection it is difficult to have sufficient amount of ground truth information that is needed in supervised learning. On the contrary it is easy to identify a few labeled patterns by the experts. In this situation to avoid wastage of available information semi-supervision is suggestible to enhance the performance of unsupervised ones. Here we present the fuzzy clustering based semi-supervised technique to detect the changes in remote sensing images that takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times. To do so two classical fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson Kessel clustering (GKC) algorithms have been used in semi-supervised way. For clustering purpose various image features are extracted using the neighborhood information of pixels. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. Results are compared with those of existing unsupervised fuzzy clustering based technique, Markov random field (MRF) & neural network based algorithms and found to be superior.