Storage and processing of massive remote sensing images using a novel cloud computing platform

Abstract

In recent years, the rapid development of remote sensing technology has proliferated high-quality images that occupy larger and larger storage spaces. Video has become widespread for environmental observation. Hence, digital data is growing exponentially, and geographic information systems must determine how to manage and process images and video effectively. Researchers cannot limit themselves to desktop PCs due to computational and storage limits. The aim of this article was to propose and implement an architectural design for a novel cloud computing platform based on two Web Coverage Service and Web Map Service interfaces from the Open Geospatial Consortium (OGC), cloud storage from Hadoop Distributed File System (HDFS), and image processing from MapReduce. Results are presented on tablet computers (Asus transformer pad) and websites. Within this framework, we implemented image management as well as simple WebGIS and created an experiment in read/write performance with four kinds of data sets (normal distribution, skew to left, skew to right, and peak in left and right). For write/read performance with HDFS, the proposed system outperformed a local file system for large files (most files ranged from 8 MB to 10 MB), with many concurrent users (simulated threads equal to 40 or 50). An observer on the ground with a touchscreen can identify central points (man-made centroids) of real-time images by tapping the tablet with a finger. A second experiment revealed that the convergence for human intervention was better than convergence for random centroids in two kinds of cloud computing environments.

Keywords:

• cloud computing
• Hadoop
• K-means
• remote sensing

Acknowledgment

This work is supported by the National Science Council of Taiwan, under grant NSC100-2625-M-035-003, NSC100-2119-M-035-001, NSC101-2625-M-035-002, and NSC101-2119-M-035-003. We also thank reviewers for their comments.