Scalable analysis and retrieval of polarimetric SAR data on elastic computing clouds
Egileak: Luigi Mascolo, Marco Quartulli, Pietro Guccione, Giovanni Nico, Igor G. Olaizola
Data: 12.11.2014
Abstract
Earth Observation (EO) mining systems aim at supporting efficient access and exploration of large volumes of image products. In this work, we address the problem of content-based image retrieval via example-based queries from Petabyte-scale EO data archives. To this end, we propose an interactive data mining system that relies on distributing unsupervised ingestion processes onto virtual machine instances in elastic, on-demand computing infrastructures that also support archive-scale content indexing via a "big data" analytics cluster-computing framework. In particular, we focus on the analysis of polarimetric SAR data, for which target decomposition theorems have proved fundamental in discovering patterns in data and in characterizing the ground scattering properties. Experiments are carried out on the publicly available UAVSAR full polarimetric data archive, whose basic products amount to about 0.64 PB of storage. We report the results of the tests performed by using a public IaaS. The obtained measures appear promising for data mapping and information retrieval applications.
BIB_text
author = {Luigi Mascolo, Marco Quartulli, Pietro Guccione, Giovanni Nico, Igor G. Olaizola },
title = {Scalable analysis and retrieval of polarimetric SAR data on elastic computing clouds},
pages = {150-153},
keywds = {
Content–Based Retrieval, Remote Sensing, Elastic Cloud Computing, Big Data, Polarimetric SAR 1
}
abstract = {
Earth Observation (EO) mining systems aim at supporting efficient access and exploration of large volumes of image products. In this work, we address the problem of content-based image retrieval via example-based queries from Petabyte-scale EO data archives. To this end, we propose an interactive data mining system that relies on distributing unsupervised ingestion processes onto virtual machine instances in elastic, on-demand computing infrastructures that also support archive-scale content indexing via a "big data" analytics cluster-computing framework. In particular, we focus on the analysis of polarimetric SAR data, for which target decomposition theorems have proved fundamental in discovering patterns in data and in characterizing the ground scattering properties. Experiments are carried out on the publicly available UAVSAR full polarimetric data archive, whose basic products amount to about 0.64 PB of storage. We report the results of the tests performed by using a public IaaS. The obtained measures appear promising for data mapping and information retrieval applications.
}
isbn = {978-92-79-43252-1 },
date = {2014-11-12},
year = {2014},
}
