Abstract
The rapid processing of mass remote sensing data put challenges on computer's processing capability. Through parallel programming environment based on message passing, parallel K-means unsupervised classification of remote sensing image with different sizes we performed in parallel environment with different computers number. The speedup and efficiency of parallel computation as well as effect of message communication on parallel unsupervised classification were analyzed. The results show that the classification speed of mass amount data parallel remote sensing image unsupervised classification has been greatly improved and the parallel unsupervised classification has effect on parallel efficiency. In the parallel programming, message communication should be minimized as possible and messages should be merged to improve computation efficiency. Rational task allocation and communication can improve performance of parallel computing.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Optik - International Journal for Light and Electron Optics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
