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Earth Observation (EO) mining aims at supporting efficient access andexploration of petabyte-scale space- and airborne remote sensing archives thatare currently expanding at rates of terabytes per day. A significant challengeis performing the analysis required by envisaged applications --- like forinstance process mapping for environmental risk management --- in reasonabletime. In this work, we address the problem of content-based image retrieval viaexample-based queries from EO data archives. In particular, we focus on theanalysis of polarimetric SAR data, for which target decomposition theorems haveproved fundamental in discovering patterns in data and characterize the groundscattering properties. To this end, we propose an interactive region-orientedcontent-based image mining system in which 1) unsupervised ingestion processesare distributed onto virtual machines in elastic, on-demand computinginfrastructures 2) archive-scale content hierarchical indexing is implementedin terms of a "big data" analytics cluster-computing framework 3) queryprocessing amounts to traversing the generated binary tree index, computingdistances that correspond to descriptor-based similarity measures between imagegroups and a query image tile. We describe in depth both the strategies and theactual implementations for the ingestion and indexing components, and verifythe approach by experiments carried out on the NASA/JPL UAVSAR fullpolarimetric data archive. We report the results of the tests performed oncomputer clusters by using a public Infrastructure-as-a-Service and evaluatingthe impact of cluster configuration on system performance. Results arepromising for data mapping and information retrieval applications.

Distributed mining of large scale remote sensing image archives on public computing infrastructures