Remote sensing image processing service flow achieving method in cloud environment

Abstract

The invention relates to the field of remote sensing image processing, in particular to a remote sensing image processing service flow achieving method in a cloud environment. The method comprises thefollowing steps that remote sensing image mirror image documents are uploaded to Openstack, so that virtual machine cluster instances subjected to co-processing of different remote sensing images inthe cloud environment are created in the Openstack; among different virtual machines, log-in is achieved without codes through SSH, and transmission of remote sensing image intermediate data among thedifferent virtual machines is achieved; after a remote sensing image processing service flow scheduling system receives a remote sensing image processing service flow job created by a remote sensingimage processing user from a Web client, through a remote sensing service BPMN process revolver, a Jenkins REST API and an openstack rest API, tasks are distributed onto the different virtual machinesfor scheduling treatment. According to the method, through virtualization isolation, it is achieved that multiple remote sensing image processing algorithms run on a same physical machine, so that the multi-core resource utilization rate of the physical machine is improved; by creating a virtual machine cluster on multiple physical machine servers, by means of the REST APIs, remote sensing task co-processing over the virtual cluster is achieved, and then a co-scheduling service flow in the cloud environment is formed.

Description

technical field [0001] The invention relates to the field of remote sensing image processing, and more specifically, to a method for realizing remote sensing image processing service flow in a cloud environment. Background technique [0002] In order to realize the efficient utilization of remote sensing resources, it is first necessary to maximize the computing throughput of remote sensing image processing under the condition of limited resources. In the prior art, since the different processing steps and processing tasks of the remote sensing image processing algorithm flow are in many cases serial processing, the remote sensing image processing engineer does not use the multi-core method to design the remote sensing image when designing the remote sensing algorithm processing algorithm. This design method will inevitably cause only one single core of the processing facility to process when running the remote sensing image processing algorithm, which reduces the utilizati...

AI Technical Summary

Problems solved by technology

This design method will inevitably result in only one single core processing in the processing facility when running the remote sensing image processing algorithm, which reduces the utilization rate of hardware resources and is not conducive to the efficient use of remote sensing resources.

Images