Abstract

Background: Owing to increased growth in satellite imagery, the development of an architecture that rapidly and efficiently identifies similar images has become crucial. Hadoop has become a de-facto platform for storing large amounts of data. Apache Spark and MapReduce have also become key frameworks for distributed processing of big data.

Objective: This paper proposes a novel Distributed Content-Based Image Retrieval (DCBIR) architecture that leverages the qualities of these engines, which were not utilized in previous studies.

Methods: Features of 40 satellite images with sizes greater than 500 MB were indexed, on a 15-node Hadoop cluster with two different databases viz. Neo4J, a graph database, and HBase, a columnar database.

Results: Performance and Scalability of both indexing and query phases, along with precision and recall were observed for both databases.

Conclusion: Experimental results show that the proposed system can efficiently perform image retrieval on large remote sensing images.

Keywords: Distributed computing, image retrieval, MapReduce, satellite images, hadoop, spark, Neo4J, HBase.

Graphical Abstract