5845054
doi
10.5281/zenodo.5845054
oai:zenodo.org:5845054
user-eu
Stelios Andreadis
CERTH-ITI
Nick Pantelidis
CERTH-ITI
Sameed Hayat
HLRS
Li Zhong
HLRS
Marios Bakratsas
CERTH-ITI
Dennis Hoppe
HLRS
Stefanos Vrochidis
CERTH-ITI
Ioannis Kompatsiaris
CERTH-ITI
Parallel DBSCAN-Martingale estimation of the number of concepts for automatic satellite image clustering
Ilias Gialampoukidis
CERTH-ITI
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Density-based clustering
Image clustering
High Performance Computing
<p>The necessity of organising big streams of Earth Observation (EO) data induces the efficient clustering of image patches, deriving from satellite imagery, into groups. Since the different concepts of the satellite image patches are not known a priori, DBSCAN-Martingale can be applied to estimate the number of the desired clusters. In this paper we provide a parallel version of the DBSCAN-Martingale algorithm and a framework for clustering EO data in an unsupervised way. The approach is evaluated on a benchmark dataset of Sentinel-2 images with ground-truth annotation and is also implemented on High Performance Computing (HPC) infrastructure to demonstrate its scalability. Finally, a cost-benefit analysis is conducted to find the optimal selection of reserved nodes for running the proposed algorithm, in relation to execution time and cost.</p>
Zenodo
2022-04-05
info:eu-repo/semantics/conferencePaper
5845053
user-eu
award_title=EOPEN: opEn interOperable Platform for unified access and analysis of Earth observatioN data; award_number=776019; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/776019; funder_id=00k4n6c32; funder_name=European Commission;
award_title=Copernicus Artificial Intelligence Services and data fusion with other distributed data sources and processing at the edge to support DIAS and HPC infrastructures; award_number=101004152; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/101004152; funder_id=00k4n6c32; funder_name=European Commission;
1642081741.230846
8335493
md5:9ddc5d6c713b3f634432634555aa840e
https://zenodo.org/records/5845054/files/Parallel DBSCAN-Martingale estimation of the number of concepts for automatic satellite image clustering.pdf
public
10.5281/zenodo.5845053
isVersionOf
doi