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.