Unsupervised detection of topographic highs with arbitrary basal shapes based on volume evolution of isocontours

In this work, an unsupervised isocontour based segmentation method is proposed, that is applied on the detection of topographic highs with arbitrary basal shapes on Digital Elevation Models (DEMs). A series of isocontour based segmentation maps is computed for decreasing altitude levels. During this process, the isocontours are gradually merged providing a topological hierarchy of highs in an inclusion tree structure. A novel formulation of a topographic high is given taking into account the volume evolution of an isocontour that starts from the top of a high and grows, as decreasing the altitude level of isocontour, until a high of higher altitude is reached. This formulation yields to a robust unsupervised algorithm that can be sequentially applied to automatically recognize and discriminate the topographic highs of a region according to the inclusion tree without any constraint on basal shapes. The proposed method is applied on real and synthetic DEMs, in order to automatically detect the exact shape of complex topographic highs and some geomorphological based features useful for high annotations, yielding high performance results, even if the highs are partially visible in the given DEM.