Processing high resolution images of urban areas with self-dual attribute filters

The application of remote sensing to the study of human settlements relies on the availability of different types of image sources which provide complementary measurements for the characterization of urban areas. By analyzing images of very high spatial resolution (metric and submetric pixel size) it is possible to retrieve information on buildings (e.g., characterizing their size and shape) and districts (e.g., assessing settlement density and urban sprawl). In this context, mathematical morphology provides a set of tools that are useful for the characterization of geometrical features in urban images.

Among those tools, attribute filters (AF) have proven to effectively extract these spatial characteristics. In this paper, we propose AF based on the inclusion tree structure as an efficient technique for generating features suitable for structure extraction in an urban environment. We address the issue by combining the area and moment of inertia attributes and proving the potential of this filter in the analysis of the data acquired by different types of sensors (i.e., Optical, LiDAR and SAR images).