Improved Chirp Scaling Algorithm for Processing Squinted Mode Synthetic Aperture Sonar Data

Fan, Naiqiang; Wang, Yingmin; lv, Yarong

doi:10.1515/cait-2016-0081

Cited by 1 publication

(1 citation statement)

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“…Faced with the explosive growth of digital image data, traditional stand-alone image processing faces many problems, such as low processing speeds and poor concurrency [1]. Therefore, the traditional image processing mode cannot evolve to meet the needs of users, and it is necessary to find a new effective image processing mode [2]. Aminsoofi provided a general reference to cover a wide range of issues related to data security in cloud computing, from accountability to data sources, identification and risk management [3].…”

Section: Introductionmentioning

confidence: 99%

Image data model optimization method based on cloud computing

Liu

Jing²,

Sun

et al. 2020

J Cloud Comp

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In the current age of data explosion, the amount of data has reached incredible proportions. Digital image data constitute most of these data. With the development of science and technology, the demand for networked work and life continues to grow. Cloud computing technology plays an increasingly important role in life and work. This paper studies the optimization methods for cloud computing image data recognition models. The parallelization and task scheduling of the remote-sensing image classification model SCSRC based on spatial correlation regularization and sparse representation are studied in a cloud computing platform. First, cloud detection technology, combined with the dynamic features of the edge overlap region, is implemented in cloud computing mode. For image edge overlap region detection, the SCSRC method is implemented on a single machine, and the time performance of the method is analysed experimentally, which provides a basis for parallelization research under the cloud computing platform. Finally, the speedup and expansion ratio of the SK-SCSRC algorithm are determined by experiment, and MR-SCSRC and SK-SCSRC are compared. The simulation results show that, compared to previous methods, the method of image edge overlap detection is more accurate and the image fusion is better, which improves the image recognition ability in the overlap region and demonstrates the performance improvement of the MR-SCSRC algorithm under scheduling. This method addresses the shortcomings of Hadoop’s existing scheduler and can be integrated into remote-sensing cloud computing systems in the future.

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Section: Introductionmentioning

confidence: 99%