一种面向海量遥感数据分类应用的并行解决方案

A parallel solution to mass remote sensing data classification and application

目前,遥感数据量呈海量增长趋势。如何在大数据环境下进行快速影像分类及信息挖掘,提升处理的业务化水平,是一个重要的研究方向。鉴于此,实现了一种高效的解决方案。首先,基于"五层十五级"数据结构,对以景为单位的影像进行离散化处理,建立以切片为单元的数据组织体系。其次,借助大数据云存储技术实现切片的集群分布式存储。其次采用了基于像元和对象的高效监督分类算法,并依据计算处理需求对集群环境下的并行架构和驱动机制进行适应性设计。最终,实现了该解决方案并以高分2号多光谱切片进行实验。结果表明:该方案在保证精度的前提下提高了分类处理的效率。

At present, as remote sensing data grows massively, how to carry out fast image classification and information mining in applications and how to improve the business level of manipulation, is an important research direction. Aiming at this problem, we propose an efficient solution. Firstly, based on ＂five-layer fifteen-level＂ data structure, we segment the image which takes a scene as a unit, then build an image data organization system based on image slices. Secondly, with the help of storage technology of large data, we realize a cluster distributed storage of slices. Thirdly, we utilize the supervised classification algorithms based on pixel and object as the processing algorithm, and make adaptive designs of parallel architecture and drive mechanism in cluster environment according to computation processing requirements. Finally, we realize the solution and carry out experiments with GF2 multi spectral slicing. The results show that the proposed solution can improve the efficiency of classification processing while maintaining the accuracy.