  Earth Observation (EO) mining aims at supporting efficient access and
exploration of petabyte-scale space- and airborne remote sensing archives that
are currently expanding at rates of terabytes per day. A significant challenge
is performing the analysis required by envisaged applications --- like for
instance process mapping for environmental risk management --- in reasonable
time. In this work, we address the problem of content-based image retrieval via
example-based queries from EO data archives. 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 characterize the ground
scattering properties. To this end, we propose an interactive region-oriented
content-based image mining system in which 1) unsupervised ingestion processes
are distributed onto virtual machines in elastic, on-demand computing
infrastructures 2) archive-scale content hierarchical indexing is implemented
in terms of a "big data" analytics cluster-computing framework 3) query
processing amounts to traversing the generated binary tree index, computing
distances that correspond to descriptor-based similarity measures between image
groups and a query image tile. We describe in depth both the strategies and the
actual implementations for the ingestion and indexing components, and verify
the approach by experiments carried out on the NASA/JPL UAVSAR full
polarimetric data archive. We report the results of the tests performed on
computer clusters by using a public Infrastructure-as-a-Service and evaluating
the impact of cluster configuration on system performance. Results are
promising for data mapping and information retrieval applications.
