摘要
为提升卫星遥感数据的单位传输速率,需要对数据进行压缩,基于此,设计一种基于深度学习算法的卫星遥感数据智能压缩方法。首先进行遥感数据块的量化处理,在合理范围之内确定压缩稀疏度,形成压缩层级,通过设定智能数据压缩周期,对数据单次压缩范围进行限制,结合深度学习算法构建遥感数据压缩模型,采用深度解压缩模式实现数据的最终处理。结果表明:本文设计方法的数据单位传输速率均控制在90 Mbps以上,应用效果较好。
In order to improve the unit transmission rate of satellite remote sensing data,it is necessary to compress the data.Based on this,this paper designs an intelligent compression method of satellite remote sensing data based on deep learning algorithm.Firstly,the remote sensing data block is quantized,the compression sparsity is determined within a reasonable range,and the compression level is formed.By setting the intelligent data compression cycle,the single compression range of the data is limited.The remote sensing data compression model is built by combining the deep learning algorithm,and the final processing of the data is realized by using the deep decompression mode.The results show that the data unit transmission rate of this design method is controlled above 90 Mbps,and the application effect is good.
作者
赵泊宁
Zhao Boning(School of Computer and Information Engineering,Heilongjiang University of Science and Technology,Harbin 150022,China)
出处
《科学技术创新》
2022年第32期73-76,共4页
Scientific and Technological Innovation
关键词
深度学习
卫星遥感
数据监测
智能压缩
压缩方法
deep learning
satellite remote sensing
data monitoring
intelligent compression
compression method
作者简介
赵泊宁(2002-),男,本科在读,研究方向:计算机类。
