  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.
