For an accurate earthquake damage assessment from very high resolution (VHR) images, contextual relations between pixels need to be included in conjunction with spectral information during the classification. To utilize the spatial information in an efficient way, specific patterns representing the earthquake-induced damage should properly be modelled. Attribute Profiles (APs) and Multi Attribute Profiles (MAPs) provide a multi-dimensional representation of an image with a successive implementation of different attribute filters, and they are able to generate the complicated features for a specific pattern. In this study, the APs and the MAPs were used for the first time to extract the additional contextual features from very high resolution satellite image of City of Bam (Iran) acquired eight days after the earthquake. The performance of the morphological attribute features was compared to the those of Haralick's features (HFs) using the k-nn classifier, and the preliminary results showed that the APs and MAPs detect the earthquake damage more accurate than the HFs.