A Distributed Storage Scheme for Remote Sensing Image based on Mapfile

Abstract

Hyperspectral image has a large amount of data and complex structure. The distributed storage of massive remote sensing data is a hot topic today; however, the current research mostly separates the image pixels and metadata, resulting in poor system cohesion and poor data access performance. At the same time, the needs of various upper-level remote sensing algorithms are not fully considered, which makes the system less available. In view of the above problems, this paper presents a distributed image storage model based on HDFS, which stores the entire image data model in a structure to improve the system cohesion, and provides a flexible data blocking strategy for upper-level applications to meet a variety of data access needs. The comparison experiments show that the storage model has better access performance than the existing schemes.

A Survey of Big Data Analytics for Smart Forestry

“…It not only effectively alleviates the pressure of massive data storage in the sensor network, but also improves the scalability of the storage system the data storage security of the sensor network. Chen [40] stores remote sensing data based on Mapfile, and improved the cohesion of the system by storing remote sensing images and remote sensing metadata into a structure in HDFS. Xiao [41] proposes a parallel computing framework for National Geographic Conditions Monitoring project of China based on spark and HDFS.…”