On buffer zone construction, the rasterization-based dilation method inevitably introduces errors, and the double-sided parallel line method involves a series of complex operations. In this paper, we proposed a parallel buffer algorithm based on area merging and MPI (Message Passing Interface) to improve the performances of buffer analyses on processing large datasets. Experimental results reveal that there are three major performance bottlenecks which significantly impact the serial and parallel buffer construction efficiencies, including the area merging strategy, the task load balance method and the MPI inter-process results merging strategy. Corresponding optimization approaches involving tree-like area merging strategy, the vertex number oriented parallel task partition method and the inter-process results merging strategy were suggested to overcome these bottlenecks. Experiments were carried out to examine the performance efficiency of the optimized parallel algorithm. The estimation results suggested that the optimization approaches could provide high performance and processing ability for buffer construction in a cluster parallel environment. Our method could provide insights into the parallelization of spatial analysis algorithm.

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Remote Sensing Information Computation has features of large amount of image data and complex algorithms, many applications require real-time response, but mainstream computers are often unable to meet their performance requirements. Regarding to this problem, a cluster-based computing platform for remote sensing information computation was designed and implemented through interconnecting the existing computing devices by network, and in support of MPI and other communication software, cluster management module which enables node management, task management, and real time monitoring and other functions was developed. On this basis, the parallel algorithms of re-projection, mean-shift multi-scale segmentation were achieved; the efficiency and performance of the cluster were tested. Experiment results show that the system can significantly improve the efficiency of remote sensing information computation with a low price.