Unsupervised change detection analysis to multi-channel scenario based on morphological contextual analysis

A novel unsupervised change detection approach for multi-spectral remote sensing data based on morphological transformation is presented. Profiles obtained by attribute filters can provide a rich multi-level analysis of the contextual information. The proposed method is based on the assumption that pixels belonging to changed areas exhibit profiles with significant differences due to a variation in their geometry, whereas pixels within unchanged areas result in similar profiles due to their similar spatial characteristics. The extension to the multi-spectral scenario is performed by applying the morphological analysis on the available bands that compose a given data set. In such scenario radiometric normalization results mandatory in order to minimize the effect due to different acquisition's conditions. To this purpose, IR-MAD is performed as pre-processing. In the paper, preliminary results obtained considering a multi-temporal Landsat ETM+ data set acquired over an agriculture area are shown.