用于遥感图像目标快速匹配识别的改进混合溢出树算法

陈彦彤; 徐伟; 朴永杰; 王灿进; 陈娟

doi:10.3788/OPE.20162409.2310

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用于遥感图像目标快速匹配识别的改进混合溢出树算法

信息科学 | 更新时间：2020-07-07

- 用于遥感图像目标快速匹配识别的改进混合溢出树算法
- Improved hybrid spill-tree algorithm for fast target matching recognition of satellite images
- 光学精密工程 2016年24卷第9期页码：2310-2317
- 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：
  
  [ "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机器视觉方面的研究。E-mail:chenyantong1@yeah.net" ]
  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.com XU Wei, E-mail: xwciomp@126.com
- 基金信息：
  
  国家863高技术研究发展计划资助项目(2012AA121502)
- DOI：10.3788/OPE.20162409.2310
  中图分类号： TP753
- 收稿日期：2016-01-18，
  
  录用日期：2016-3-4，
  
  纸质出版日期：2016-09
- 稿件说明：
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陈彦彤, 徐伟, 朴永杰, 等. 用于遥感图像目标快速匹配识别的改进混合溢出树算法[J]. 光学精密工程, 2016,24(9):2310-2317.

Yan-tong CHEN, Wei XU, Yong-jie PIAO, et al. Improved hybrid spill-tree algorithm for fast target matching recognition of satellite images[J]. Optics and precision engineering, 2016, 24(9): 2310-2317.
陈彦彤, 徐伟, 朴永杰, 等. 用于遥感图像目标快速匹配识别的改进混合溢出树算法[J]. 光学精密工程, 2016,24(9):2310-2317. DOI： 10.3788/OPE.20162409.2310.

Yan-tong CHEN, Wei XU, Yong-jie PIAO, et al. Improved hybrid spill-tree algorithm for fast target matching recognition of satellite images[J]. Optics and precision engineering, 2016, 24(9): 2310-2317. DOI： 10.3788/OPE.20162409.2310.

摘要

提出一种基于标记的混合溢出树（SHSPT）特征匹配算法，用于遥感图像的目标匹配识别。针对特征数据建立和预处理，提出了基于中心点的数据分割方法，通过定义数据密集区域的中心，舍去边缘稀疏数据，提取出分割后的数据。进行特征匹配时，使用二进制数组表示数据空间，标记分割后的特征向量数据，通过比特操作计算特征向量间的距离，缩短计算时间。最后对特征匹配方法进行改进，采用待匹配特征距离的均值代替尺度不变特征变换（SIFT）匹配算法的次临近特征距离，从而得到更多的匹配点。实验证明，基于标记的混合溢出树特征匹配算法占用内存空间比传统的混合溢出树算法减少约68%，匹配准确度与原算法接近，匹配时间平均缩短了约32.8%，解决了航天遥感图像数据量大，特征维数较高，匹配识别时间长，占用计算机内存大等问题。

Abstract

An improved Hybrid Spill-tree algorithm based on the signed method defined as Signed Hybrid Spill-tree (SHSPT) was proposed for target matching of remote sensing images. For establishing data and preprocessing

a data separate method based on a center point was proposed

the separated data were extracted by defining the center of dense data

and the edge data were abandoned. In the feature matching

binary array were used to express the data space and to mark the feature vector. Then

the bit operation was used to compute the distance between the feature vectors and to shorten the computing time. Finally

the feature matching algorithm was improved. The average value of the feature distance was used to replace the secondary characteristic distance from the Scale Invariant Feature Transform(SIFT)matching algorithm to obtain more matching points. The test results show that the computer memory by proposed algorithm is reduced 68% than that of traditional hybrid spill-tree method

and matching accuracy is closed to that of the traditional one. In addition

the method reduces 32.8% matching time. It solves the problems of remote sensing images in larger data amounts

higher dimensions

longer matching time and larger computer memory.

关键词

Keywords

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