Automatic morphological attribute profiles

Attribute profiles (APs) have increasingly been receiving more attention over the last years, as they are able to extract and model spatial information that is useful for the analysis of remote sensing images of very high spatial resolution (VHR). However, one of the major issues in employing APs is the choice of a proper range of thresholds, able to provide a representative and non-redundant multi-level image decomposition. This paper presents a novel method for the automatic selection of adequate thresholds to compute the AP. A new concept of cumulative function, which can be seen as an extension of the basic notion of granulometry, is introduced. In particular, different information on the spatial context is achieved according to the measure used for computing the cumulative function, which is computed on the AP composed by considering all possible values of the attribute. The proposed approach aims at selecting the set of thresholds that provides the best approximation of the resulting cumulative function based on the chosen measure. Experimental analysis carried out on a very high resolution image shows the effectiveness of the presented strategy in providing a set of thresholds able to retain the salient spatial structures in the scene.