Improving Change Detection Results of IR-MAD by Eliminating Strong Changes

This letter examines the effect of the prior elimination of strong changes on the results of change detection in bitemporal multispectral images using the previously published iteratively reweighted multivariate alteration detection (IR-MAD) method. An initial change mask is calculated by identifying strong changes between two images. By using the mask and hence eliminating the strong changes from the analysis, the IR-MAD method is able to identify a better no-change background. This effect is demonstrated on a multitemporal Landsat Enhanced Thematic Mapper Plus data set from an agricultural region in Germany with substantial improvement in the results even for the scenes which have a large number of changes.