A generalized statistical model for binary change detection in multispectral images

Recently, a thresholding method based on the Rayleigh-Rice mixture has been proposed for solving binary change detection problems in multispectral image pairs. However, when images acquired by the last generation of multispectral scanners having high radiometric resolution are considered, the distribution fitting is still not satisfactory and computed thresholds remain quite distant from the optimal values. The main reason for this seems to be that in all previous approaches the unchange class is modeled as a single class. Instead, both practice and recent studies showed that this is not the case for new generation data. In this work, we propose a generalized statistical model for the difference image that allows the unchange class to be complex. The resulting model has more degrees of freedom, therefore it better fits real distributions and returns almost optimal thresholds for binary decision also with high radiometric resolution images.