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Change detection is a standard tool to extract and analyze the earth’s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis (CVA) have an exceptional advantage of discriminating change in terms of change magnitude and vector direction from multispectral bands. The estimation of precise threshold is one of the most crucial task in CVA to separate the change pixels from unchanged pixels because overall assessment of change detection method is highly dependent on selected threshold value. In recent years, integration of fuzzy clustering and remotely sensed data have become appropriate and realistic choice for change detection applications. The novelty of the proposed model lies within use of fuzzy maximum likelihood classification (FMLC) as fuzzy clustering in CVA. The FMLC based CVA is implemented using diverse threshold …