基于云平台的SAR图像存储与识别系统

Jia Zhai, Xiaodan Xie, Yusheng Jia
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引用次数: 0

摘要

随着现代战场环境的日益复杂,传统的SAR识别方法过于依赖训练数据源,难以实现鲁棒性和通用性,已不能满足现代战争的需求。因此,如何对快速增长的SAR图像进行自动解译成为一个迫切需要解决的问题。大数据、云计算和深度学习技术的出现,使得大容量SAR图像的自动智能解译成为可能。本文提出了一种基于云平台的SAR图像存储与识别系统,用于从复杂场景中自动获取和识别各种军事目标。该系统将基于云的平台与深度学习方法相结合,实现数据的实时识别分析和批量处理。分布式存储与云服务无缝融合,满足大规模数据识别和管理需求。评估表明,该方法在性能、存储和容错性方面更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The SAR Image Storage and Recognition System Based on Cloud Platform
As the modern battlefield environment has become increasingly complex, the traditional SAR recognition methods are too dependent on the training data source to be robust and universal, which makes it can not meet the demand of modern warfare. So how to automatically interpret the fast increased SAR images becomes an urgent problem. The emergence of big data, cloud computing and deep learning technology makes the automatic and intelligent interpretation of large volume of SAR images become possible. This paper proposes a SAR image storage and recognition system based on cloud platform to automatically obtain and identify all kinds of military targets from a complex scene. The system combines the cloud-based platform and deep learning method to achieve real-time recognizing analyses and batch processing of data. The seamless integration of distributed storage and cloud services meets the needs of large-scale data recognition and management requirements. The assessment shows that the method is more efficient in terms of performance, storage, and fault tolerance.
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