A Parallel Mosaic Method for Large-Scale Remote Sensing Images Based on Task Tree

Abstract

The invention relates to a task tree-based large scale remote-sensing image parallel embedding method, which comprising the following steps: 1) constructing an embedding task tree based on an abutting relation and a recursion task division, and constructing an embedding task tree according to the constructing method of a balance binary tree; 2) expressing a DAG (directed acyclic graph) model of the embedding task tree; 3) dynamically dispatching the embedding task tree based on CPDS-SQ (a dynamic DAG dispatching strategy based on a core path and a state queue); and 4) parallel processing a plurality of embedding tasks. The method has the following benefits: the data dependency relationship of a series of embedding tasks is decoupled from the MPI-based (message passing interface) parallel embedding realization process and is dispatched by the dynamic task tree for task dependency relationship control, so that the parallel control logic and parallel realization of the parallel embedding process can be greatly simplified, and the parallelism of large scale embedding is explored to the maximum extent to greatly improve the treatment performance and expansibility of large-scale embedding.

Description

technical field [0001] The invention relates to a task tree-based parallel mosaic method for large-scale remote sensing images. Background technique [0002] Large-scale remote sensing image mosaicking has been widely concerned and applied to scientific research on tropical rainforests, land use, and environmental changes in large regions and even globally. Remote sensing image mosaic usually seamlessly stitches a large amount of remote sensing image data with overlapping regions into a geometrically accurate, radially balanced and continuous large mosaic map, thus providing a continuous and global view for the entire large geographical area. However, when the mosaic scale is expanded to a large region, the whole country, or even the whole world, large-scale mosaic faces challenges: massive remote sensing image data, complex remote sensing image mosaic processing process, astonishing computing power requirements, and a large number of predecessors and follow-up data dependen...

AI Technical Summary

Problems solved by technology

However, when facing the problem of large-scale remote sensing image mosaicking, purely relying on low-level parallel modes such as MPI or OpenMP will make the parallel processing process of large-scale remote sensing image mosaicing extremely complicated, and the extra tasks introduced by the dependencies between The communication and synchronization logic between computing nodes will be extremely complex and difficult

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