This title appears in the Scientific Report : 2017 
Automatic Attribute Profiles
Cavallaro, Gabriele (Corresponding author)
Falco, Nicola / Dalla Mura, Mauro / Benediktsson, Jon Atli
Jülich Supercomputing Center; JSC
IEEE transactions on image processing, 26 (2017) 4, S. 1859 - 1872
New York, NY IEEE 2017
10.1109/TIP.2017.2664667
Journal Article
Enabling Intelligent GMES Services for Carbon and Water Balance Modeling of Northern Forest Ecosystems
Data-Intensive Science and Federated Computing
Please use the identifier: http://dx.doi.org/10.1109/TIP.2017.2664667 in citations.


Morphological attribute profiles are multilevel decompositions of images obtained with a sequence of transformations performed by connected operators. They have been extensively employed in performing multiscale and region-based analysis in a large number of applications. One main, still unresolved, issue is the selection of filter parameters able to provide representative and non-redundant threshold decomposition of the image. This paper presents a framework for the automatic selection of filter thresholds based on Granulometric Characteristic Functions (GCFs). GCFs describe the way that non-linear morphological filters simplify a scene according to a given measure. Since attribute filters rely on a hierarchical representation of an image (e.g., the Tree of Shapes) for their implementation, GCFs can be efficiently computed by taking advantage of the tree representation. Eventually, the study of the GCFs allows the identification of a meaningful set of thresholds. Therefore, a trial and error approach is not necessary for the threshold selection, automating the process and in turn decreasing the computational time. It is shown that the redundant information is reduced within the resulting profiles (a problem of high occurrence, as regards manual selection). The proposed approach is tested on two real remote sensing data sets, and the classification results are compared with strategies present in the literature.