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中文摘要：
面向海量高分辨率遥感影像数据快速发布需求,针对当前云环境下遥感影像数据并行重采样存在的难题,结合云平台MapReduce并行计算框架特性和遥感影像数据处理特点,提出了一种基于预分片的遥感影像数据并行重采样方法,通过预分片机制有效实现了该框架中对影像数据分片和并行重采样任务的控制,解决了MapReduce难以用于并行处理非结构化、具有空间位置特征的遥感影像数据的问题,从而实现了云环境下遥感影像数据的高效并行重采样.通过在开源云平台Hadoop上的实验和分析表明,该方法具有良好的重采样性能,能够实现高分辨率遥感影像数据的高效重采样.
英文摘要：
In order to solve the problem of parallel resampling of remote sensing image data in cloud computing, which is the basis for rapid publication of massive remote sensing image date, a parallel resampling method of remote sensing data based on pre-partitioning was proposed in combination with the features of MapReduce parallel computing and the characteristics of remote sensing image data processing. Through the pre-partitioning mechanism, the image data splitting and parallel resampling tasks can be effectively controlled, and the problem of MapReduce framework application in the unstructured remote sensing data with spatial location features processing was solved, thereby, the efficient parallel resampling of remote sensing image data in cloud computing environment is implemented. In the experiment, a parallel resampling flow on the open-source Hadoop platform was designed according to the parallel resampling method of remote sensing data based on pre-partitioning. The experiment and analysis show that the parallel resampling method has a good resampling performance and is capable of achieving the efficient resampling of high resolution remote sensing image data in cloud computing environment.
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