TY - GEN
T1 - Automatic threshold selection for profiles of attribute filters based on granulometric characteristic functions
AU - Cavallaro, Gabriele
AU - Falco, Nicola
AU - Mura, Mauro Dalla
AU - Bruzzone, Lorenzo
AU - Benediktsson, Jón Atli
N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Morphological attribute filters have been widely exploited for characterizing the spatial structures in remote sensing images. They have proven their effectiveness especially when computed in multi-scale architectures, such as for Attribute Profiles. However, the question how to choose a proper set of filter thresholds in order to build a representative profile remains one of the main issues. In this paper, a novel methodology for the selection of the filters’ parameters is presented. A set of thresholds is selected by analysing granulometric characteristic functions, which provide information on the image decomposition according to a given measure. The method exploits a tree (i. e., min-, max-or inclusion-tree) representation of an image, which allows us to avoid the filtering steps usually required prior the threshold selection, making the process computationally effective. The experimental analysis performed on two real remote sensing images shows the effectiveness of the proposed approach in providing representative and non-redundant multi-level image decompositions.
AB - Morphological attribute filters have been widely exploited for characterizing the spatial structures in remote sensing images. They have proven their effectiveness especially when computed in multi-scale architectures, such as for Attribute Profiles. However, the question how to choose a proper set of filter thresholds in order to build a representative profile remains one of the main issues. In this paper, a novel methodology for the selection of the filters’ parameters is presented. A set of thresholds is selected by analysing granulometric characteristic functions, which provide information on the image decomposition according to a given measure. The method exploits a tree (i. e., min-, max-or inclusion-tree) representation of an image, which allows us to avoid the filtering steps usually required prior the threshold selection, making the process computationally effective. The experimental analysis performed on two real remote sensing images shows the effectiveness of the proposed approach in providing representative and non-redundant multi-level image decompositions.
KW - Connected filters
KW - Mathematical morphology
KW - Threshold selection
KW - Tree representations
UR - https://www.scopus.com/pages/publications/84945964825
U2 - 10.1007/978-3-319-18720-4_15
DO - 10.1007/978-3-319-18720-4_15
M3 - Conference contribution
SN - 9783319187198
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 169
EP - 181
BT - Mathematical Morphology and its Applications to Signal and Image Processing - 12th International Symposium, ISMM 2015, Proceedings
A2 - Najman, Laurent
A2 - Talbot, Hugues
A2 - Benediktsson, Jon Atli
A2 - Chanussot, Jocelyn
PB - Springer Verlag
T2 - 12th International Symposium on Mathematical Morphology, ISMM 2015
Y2 - 27 May 2015 through 29 May 2015
ER -