Unsupervised change detection from remote sensing images using hybrid genetic FCM

This paper presents a new technique for unsupervised change detection in bitemporal remote sensing images using spectral change difference images and hybrid genetic FCM. The proposed method works in three steps. In the first step, three spectral change difference images:absolute value difference image, ratio image and log ratio image are computed. In the next step, a feature vector space is created using PCA. Finally, the change detection is obtained by dividing the feature vector space into two clusters using genetic FCM. The validity of the clusters is measured by DB index. The parts of image of Reno-Lake Tahoe area was used as data set for the performance evaluation of proposed algorithm. The results obtained were compared with EM based, MRF based and NSCT methods. The results verify that the proposed algorithm provides superior results than the other existing methods.