Efficient GPU computing framework of cloud filtering in remotely sensed image processing

For optical remote sensing images, an effective method to reduce or eliminate the impact of clouds is important. With big data input and real-time processing demands, efficient parallelization strategies are essential for high performance computing on multi-core systems. This paper proposes an efficient high performance parallel computing framework for cloud filtering and smoothing. A comparison and benchmarking of two parallel algorithms for cloud filtering that incorporates spatial smoothing solved by two-dimensional dynamic programming is implemented. The experiments were carried out on an NVIDIA GPU accelerator with evaluations of approximation, parallelism and performance. The test results show significant performance improvements with high accuracy compared with sequential CPU implementation, and can be applied to other multi-core systems.