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.