基于吉林一号遥感图像的星载目标快速识别系统

徐伟; 陈彦彤; 朴永杰; 王绍举

doi:10.3788/OPE.20172501.0255

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基于吉林一号遥感图像的星载目标快速识别系统

信息科学 | 更新时间：2020-07-07

- 基于吉林一号遥感图像的星载目标快速识别系统
- Target fast matching recognition of on-board system based on Jilin-1 satellite image
- Editorial Office of Optics and Precision Engineeri 2017年25卷第1期页码：255-262
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
- 作者简介：
  
  徐伟(1981-), 男, 黑龙江大庆人, 博士, 研究员, 2003年于吉林大学获得学士学位, 2008年于中国科学院长春光学精密机械与物理研究所获得博士学位, 主要从事星载一体化卫星技术及高可靠一体化航天电子学系统等方面的研究。E-mail:xwciomp@126.comE-mail:xwciomp@126.com
  [ "; 陈彦彤(1989-), 男, 辽宁沈阳人, 博士研究生, 2012年于吉林大学获得学士学位, 主要从事模式识别及遥感图像处理方面的研究。E-mail:chenyantong1@yeah.net" ]
- 基金信息：
  
  高技术研究发展计划资助项目(No.2012AA121502)
- DOI：10.3788/OPE.20172501.0255
  中图分类号： TP753
- 收稿日期：2016-04-08，
  
  录用日期：2016-6-1，
  
  纸质出版日期：2017-01-25
- 稿件说明：
移动端阅览
徐伟, 陈彦彤, 朴永杰, 等. 基于吉林一号遥感图像的星载目标快速识别系统[J]. Editorial Office of Optics and Precision Engineeri, 2017,25(1):255-262.

Wei XU, Yan-tong CHEN, Yong-jie PIAO, et al. Target fast matching recognition of on-board system based on Jilin-1 satellite image[J]. Optics and precision engineering, 2017, 25(1): 255-262.
徐伟, 陈彦彤, 朴永杰, 等. 基于吉林一号遥感图像的星载目标快速识别系统[J]. Editorial Office of Optics and Precision Engineeri, 2017,25(1):255-262. DOI： 10.3788/OPE.20172501.0255.

Wei XU, Yan-tong CHEN, Yong-jie PIAO, et al. Target fast matching recognition of on-board system based on Jilin-1 satellite image[J]. Optics and precision engineering, 2017, 25(1): 255-262. DOI： 10.3788/OPE.20172501.0255.

摘要

针对传统遥感图像地面目标识别系统图像获取周期长，信息实时性差等问题，设计星载目标快速识别系统，用于卫星在轨快速识别，提出改进的基于快速视网膜关键点（FREAK）的特征匹配识别算法，解决遥感图像数据量大、背景复杂的问题。介绍了星载目标快速识别系统的工作原理，提出简化的FREAK特征提取模型，将原有算法的七层模型减少为四层，用于快速提取出遥感图像中目标特征；利用二进制量化空间将高维特征数据量化为二维数据，提高算法的准确度；最后通过匹配，快速识别出遥感目标。实验结果表明，识别算法的准确度平均提高2.3%，识别用时缩短约27.8%，满足遥感卫星在轨目标快速识别的要求。

Abstract

Aiming at problems such as long cycle and insufficient real time information in traditional remote sensing ground target image recognition system

an on-board target fast matching recognition platform is designed for fast on-orbit satellite recognition

and an improved feature matching recognition algorithm based on fast retinal key points (FREAK) is proposed to solve the problems of complex backgrounds and large amount of data in remote sensing image

First

we introduce the principle of on-board target recognition system and propose the simplified FREAK feature extraction model

and then we reduce the model of original algorithm from seven floors to four to quickly extract target features in remote sensing image. And then the high-dimensional feature data is quantified into two-dimensional data using binary quantization space

thus improving the accuracy of the algorithm; finally

the remote targets are recognized quickly by matching. The experimental results show that the matching accuracy can be increased by 2.3%

and matching time can be reduced by 27.8%. It can meet the requirements of quick identification of remote sensing satellite on-orbit targets.

关键词

Keywords

references

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