An improved tile-based scalable distributed management model of massive high-resolution satellite images

TL;DR

This paper introduces a scalable distributed management model for massive high-resolution satellite images, utilizing a Hadoop-based framework with NoSQL databases to improve data storage and retrieval efficiency.

Contribution

It presents a novel distributed architecture combining a unified metadata file, pyramid model, and Hilbert curve indexing for large-scale remote sensing data management.

Findings

Effective handling of massive high-resolution satellite images.

Improved data management performance over traditional methods.

Suitable for large-scale remote sensing Big Data applications.

Abstract

The amount of remote sensing (RS) data has increased at an unexpected scale, due to the rapid progress of earth-observation and the growth of satellite RS and sensor technologies. Traditional relational databases attend their limit to meet the needs of high-resolution and large-scale RS Big Data management. As a result, massive RS data management is currently one of the most imperative topics. To address this problem, this paper describes a distributed architecture for big RS data storage based on a unified metadata file, pyramid model, and Hilbert curve for data composition and indexing using NoSQL databases (i.e, Apache Hbase). In this paper, a Hadoop-based framework in AzureInsight cloud platform is designed to manage massive RS data in a parallel and distributed way. Experimental results prove that our method has the potential to overcome the weakness of traditional methods. The proposed model is suitable for massive high-resolution image data management.