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@ARTICLE{Cavallaro:840596,
author = {Cavallaro, Gabriele and Falco, Nicola and Dalla Mura, Mauro
and Benediktsson, Jon Atli},
title = {{A}utomatic {A}ttribute {P}rofiles},
journal = {IEEE transactions on image processing},
volume = {26},
number = {4},
issn = {1941-0042},
address = {New York, NY},
publisher = {IEEE},
reportid = {FZJ-2017-08101},
pages = {1859 - 1872},
year = {2017},
abstract = {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.},
cin = {JSC},
ddc = {004},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {512 - Data-Intensive Science and Federated Computing
(POF3-512) / NORTH STATE - Enabling Intelligent GMES
Services for Carbon and Water Balance Modeling of Northern
Forest Ecosystems (606962)},
pid = {G:(DE-HGF)POF3-512 / G:(EU-Grant)606962},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000398976000006},
doi = {10.1109/TIP.2017.2664667},
url = {https://juser.fz-juelich.de/record/840596},
}