基于HBase的遥感数据分布式存储与查询方法研究

遥感影像的存储与查询是地理信息处理中重要的内容，在海量遥感影像的实时处理中发挥着重要作用。针对传统的遥感影像处理中存在单节点故障、扩展性低和处理效率低等问题，提出了一种基于HBase的遥感数据分布式存储与查询方案。该方法首先采用均匀网格对遥感影像进行划分，并根据划分结果设计了一种基于网格ID和Hilbert曲线相结合的索引方案。然后，通过利用HBase的过滤机制设计了过滤列族，达到了在查询时筛选数据的目的。另外，采用MapReduce的并行处理方法对影像数据进行并行写入和查询。实验结果表明，与MySQL和MapFile相比，该方法可以有效地提高数据的写入和查询速度，且具有较好的可扩展性。

A distributed storage and query method of remote sensing data based on HBase

The storage and query of remote sensing image is an important content in geographic information processing and plays an important role in the real time processing of massive remote sensing images. Aiming at the problem of single node failure, low scalability and low efficiency in traditional remote sensing image processing, this paper proposes a distributed storage and query scheme of remote sensing data based on HBase. In this method, the remote sensing image is divided by uniform mesh, and an index scheme based on grid ID and Hilbert curve is designed according to the block information. Then, the HBase filter mechanism is used to design the filter column family so as to achieve the purpose of screening data in the query. In addition, the MapReduce parallel processing method is adopted to write and query image data. The experimental results show that, compared with MySQL and MapFile, the proposed method can improve the query speed of data and has good scalability.