Clustering of Remote Sensing (RS) images is one of the most accurate methods for land cover Change Detection (CD). Despite its popularity for unsupervised CD, the necessity to determine the number of clusters is the main shortcoming of Fuzzy C-Means(FCM) for CD. Clustering the absolute difference image to pre-defined two clusters (includes change versus no-change), which is called binary FCM, is a conventional strategy to mitigate the cluster number problem. This strategy suffers from low CD accuracy because of its inability to detect multiple changes in RS images. In this paper, a novel framework for unsupervised CD is proposed, which is called Multiple FCM (MFCM). In this method, the optimal cluster number of MFCM is determined by analyzing the cluster-validity indices when clustering the difference image. After clustering the difference image, binarizing of the clustered image produces the Binary Change Map (BCM). The performance of the proposed approach along with two state-of-the-art binary CD methods (BFCM and PCA-Kmeans) were evaluated on two different datasets with 5 and 2 real cluster numbers. The implementation results show that the proposed approach not only detects multiple land cover changes but also improves the binary change detection accuracy. Utilizing the proposed MFCM, the total errors of BCM reduced by 20% and 24% as compared to BFCM and PCA- Kmeans methods in the first dataset with multiple land cover changes. In the second dataset, which has only two real clusters no significant difference was observed between the results of the proposed method and reference approaches.

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