A Model of Parallel Mosaicking for Massive Remote Sensing Images Based on Spark

Abstract

Image mosaicking is an important part of remote sensing image processing and plays a vital role in the analysis of trans-regional remote sensing images. In order to solve the problems of low utilization of nodes and frequent data I/O in traditional parallel mosaicking algorithms, we propose a parallel mosaicking algorithm based on Apache Spark. First, multi-nodes parallel computation for image overlapping region estimation are implemented in the algorithm. Then, we self-define the Resilient Distributed Data sets (RDD) for remote sensing image processing, and use the three key steps of the image mosaicking, including overlapping region estimation, image registration, and image fusion, which are as the transformation-type operators of the self-defined RDD (the self-defined RDD is what we get by extending the functionality of RDD in Spark). Finally, the parallel processing of image mosaicking is realized by calling the operators of self-defined RDD with the method of implicit conversion. Experimental results show that the parallel mosaicking algorithm of massive remote sensing image based on Spark can effectively improve the mass data image mosaicking efficiency on the basis of guaranteeing the image mosaicking effect.

Keywords

• Remote sensing image
• parallel mosaicking
• spark
• self-defined RDD