Hyperspectral change detection using IR-MAD and feature reduction

A method for change detection between two hyperspectral datasets is presented. The iteratively reweighted multivariate alteration detection (IR-MAD) method is used for change detection. The strong changes are first eliminated based on the principal component analysis (PCA) of the difference image and IR-MAD is applied on the datasets after feature reduction with the PCA of the original bands. The method is demonstrated on a bitemporal hyperspectral dataset. The results show good correlation with ground truth.