  The amount of remote sensing data available to applications is constantly
growing due to the rise of very-high-resolution sensors and short repeat cycle
satellites. Consequently, tackling computational complexity in Earth
Observation information extraction is rising as a major challenge. Resorting to
High Performance Computing (HPC) is becoming a common practice, since it
provides environments and programming facilities able to speed-up processes. In
particular, clusters are flexible, cost-effective systems able to perform
data-intensive tasks ideally fulfilling any computational requirement. However,
their use typically implies a significant coding effort to build proper
implementations of specific processing pipelines. This paper presents a generic
framework for the development of RS images processing applications targeting
cluster computing. It is based on common open sources libraries, and leverages
the parallelization of a wide variety of image processing pipelines in a
transparent way. Performances on typical RS tasks implemented using the
proposed framework demonstrate a great potential for the effective and timely
processing of large amount of data.
