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Ray-tracing, can produce high-quality images, however, the use of ray-tracing has been limited due to its high demands on computational power and memory bandwidth, especially in the case of satellite imagery. In this paper, we propose a scalable parallel ray tracing algorithm of satellite imagery on a cluster of multi-core architecture. The algorithm combines demand-driven and data-driven models in order to reduce communication on the cost of maintaining redundant data. In order to make the most of computer's parallelism and increase computational efficiency, it combines two levels of parallelisms, the TLP parallelism brought by the many core architecture and the inter-node parallelism via MPI and OpenMP. Experiment results show that the algorithm is highly scalability.