采用目标区域互信息的星空图像配准

杜斯亮; 杨博; 王密; 方圣辉

doi:10.3788/OPE.20162402.0406

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采用目标区域互信息的星空图像配准

信息科学 | 更新时间：2020-08-13

- 采用目标区域互信息的星空图像配准
- Stellar image registration based on mutual information in object area
- 光学精密工程 2016年24卷第2期页码：406-412
- 作者机构：
  
  1. 武汉大学遥感信息工程学院,湖北武汉,430079
  2. 武汉大学计算机学院,湖北武汉,430079
  3. 武汉大学测绘遥感信息工程国家重点实验室,湖北武汉,430079
  4. 地球空间信息技术协同创新中心,湖北武汉,430079
- 作者简介：
- 基金信息：
  
  国家973重点基础研究发展计划资助项目(No.2014CB744201)()
- DOI：10.3788/OPE.20162402.0406
  中图分类号： TP391.4;TP751.1
- 收稿日期：2015-11-06，
  
  修回日期：2015-12-14，
  
  纸质出版日期：2016-02-25
- 稿件说明：
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杜斯亮, 杨博, 王密等. 采用目标区域互信息的星空图像配准[J]. 光学精密工程, 2016,24(2): 406-412

DU Si-liang, YANG Bo, WANG Mi etc. Stellar image registration based on mutual information in object area[J]. Editorial Office of Optics and Precision Engineering, 2016,24(2): 406-412
杜斯亮, 杨博, 王密等. 采用目标区域互信息的星空图像配准[J]. 光学精密工程, 2016,24(2): 406-412 DOI： 10.3788/OPE.20162402.0406.

DU Si-liang, YANG Bo, WANG Mi etc. Stellar image registration based on mutual information in object area[J]. Editorial Office of Optics and Precision Engineering, 2016,24(2): 406-412 DOI： 10.3788/OPE.20162402.0406.

摘要

提出了采用目标区域互信息的测度方法对星图进行精确配准以解决星图中存在噪声、伪星点、星点稀疏以及星图间的旋转等问题。首先对星图进行图像分割

检测出星点目标并对星点进行二值化处理;然后基于互信息配准模型

在含星点的目标区域上

利用Powell算法将最大互信息作为目标函数来指导图像间最优变换参数的搜索。分析了适宜于互信息测度配准的星点分割算法

并论证了采用目标区域互信息的星图配准的可行性。对提出的算法与标准的互信息配准算法进行了对比。结果表明:提出算法的时间消耗与图像中星的数量有关

在图像大小为1000×1000时

提出算法的加速比为标准算法的3.4倍。该算法在星图中存在噪声、伪星点、星点稀疏和旋转的情况下仍能进行准确配准

50组实拍星图配准误差平均值为0.1382 pixel

满足了星空图像对精确配准的要求。

Abstract

A measuring algorithm based on mutual information in an object area was proposed to register stellar images with noises

pseudo stars

sparse stars and the rotation between the images. Firstly

a stellar image was segmented to extract star points in the stellar image and the star points were processed with binarization. Then

on the basis of the mutual information model

the Powell algorithm was used to guide the search of the best transformation parameters in the object area by taking the maximal mutual information as the aim function. Furthermore

the star segmentation method suitable for the proposed stellar registration algorithm was analyzed and the feasibility of the proposed algorithm was verified. The proposed algorithm was compared with normal mutual information registration algorithm

and the results show that the time consuming is relative to the number of stars in image

and the speed up ratio is up to 3.4 times that of the normal mutual information registration algorithm when the image size is 1000×1000. Experimental results demonstrate that the proposed algorithm achieves a high precision stellar registration with noises

pseudo stars

sparse stars and rotation. The average error of 50 groups of real stellar images is 0.1382 pixel. It concludes that the algorithm meets the requirements of space target detection for image registration.

关键词

Keywords

references

宣丽,李大禹,刘永刚. 液晶自适应光学在天文学研究中的应用展望[J]. 液晶与显示,2015,30(1):1-9. XUAN L,LI D Y,LIU Y G. Prospect of liquid crystal adapative optics in astronomy application[J]. Chinese Journal of Liquid Crystals and Displays,2015,30(1):1-9.(in Chinese)

张春华,陈标,周晓东. 运动背景星空图像中小目标的运动轨迹提取算法[J]. 光学精密工程,2008, 16(3):524-530. ZHANG CH H,CHEN B,ZHOU X D. Small target trace acquisition algorithm for sequence star images with moving background[J]. Opt. Precision Eng., 2008, 16(3):524-530.(in Chinese)

RUIZ-DEL-SOLAR J, LONCOMILA P, ZORZI P. Applying SIFT Descriptors to Stellar Image Matching[M]. Berlin:Springer-Verlag Berlin, 2008:5197, 618-625.

翟优,曾峦,熊伟. 基于不变特征描述符实现星点匹配[J]. 光学精密工程,2012, 20(11):2531-2539. ZHAI Y,ZENG L,XIONG W. Star matching based on invariant feature descriptor[J]. Opt. Precision Eng., 2012, 20(11):2531-2539.(in Chinese)

韩艳丽,刘峰. 基于三角形匹配的空间小目标检测算法[J]. 红外与激光工程,2014,43(9):3134-3140. HAN Y L,LIU F. Small targets detection algorithm based on triangle match space[J]. Infrared and Laser Engineering,2014,43(9):3134-3140.(in Chinese)

沈同圣,张健,娄树理. 面向目标检测的空间观测图像精确配准[J]. 光学精密工程,2014, 22(8):2205-2213. SHEN T SH,ZHANG J,LOU SH L. Precise registration of space observation images for target detection[J]. Opt. Precision Eng., 2014, 22(8):2205-2213.(in Chinese)

张健,娄树理,任建存. 基于时序多帧投影的空间目标检测算法[J]. 激光与红外,2015,45(1):88-93. ZHANG J,LOU SH L,REN J C. Space target detection algorithm based on sequential multi-frames projection[J]. Opt. Precision Eng., 2015,45(1):88-93.(in Chinese)

张红颖,张加万,孙济洲. 改进Demons算法的非刚性医学图像配准[J]. 光学精密工程,2007,15(1):145-150. ZHANG H Y,ZHANG J W,SUN J ZH. Non-rigid medical image registration based on improved Demons algorithm[J]. Opt. Precision Eng., 2007,15(1):145-150.(in Chinese)

武治国,李桂菊. 动态目标识别中的实时复杂巡航场景运动检测[J]. 液晶与显示,2014,29(5):844-849. WU ZH G,LI G J. Real-time complex cruise scene detection technology in target recognition[J]. Chinese Journal of Liquid Crystals and Displays,2014,29(5):844-849.(in Chinese)

王金玲,贺小军,宋克非. 采用区域互信息的多光谱与全色图像融合算法[J]. 红外与激光工程,2014,43(8):2757-2764. WANG J L,HE X J,SONG K F. Image fusion algorithm of multi-spectral and panchromatic images adopting region mutual information[J]. Infrared and Laser Engineering,2014,43(8):2757-2764.(in Chinese)

AN L, LI M, ZHANG P, et al.. Multicontextual mutual information data for SAR image change detection[J]. IEEE Geoscience and Remote Sensing Letters, 2015,12(9):1863-1867.

LIEBE C C. Accuracy performance of star trackers-A tutorial[J]. IEEE Transactons on Aerospace and Electronic Systems,2002, 38(2):587-599.

许威. 星点快速提取与高精度定位技术研究[D]. 杭州:浙江大学, 2013. XU W. Star Fast Extraction and High Accuracy Centroid Estimation of Star Camera[D]. Hangzhou:Zhejiang University, 2013.(in Chinese)

乔玉龙,赵源萌,张存林,等. 基于混合Powell法的太赫兹/可见光双波段图像配准[J]. 光学学报,2015,35(2):159-165. QIAO Y L,ZHAO Y M,ZHANG C L,et al..Terahertz/visible dual-band image registration based on hybrid Powell algorithm[J]. Acta Optica Sinica,2015,35(2):159-165.(in Chinese)

焦继超,赵保军,唐林波. 一种基于非降采样Contourlet变换和MLESAC的星空图像配准算法[J]. 系统工程与电子技术,2010, 32(12):2686-2690. JIAO J CH,ZHAO B J,TANG L B. Space image registration algorithm based on nonsubsampled Contourlet transform and MLESAC[J]. Systems Engineering and Electronics,2010, 32(12):2686-2690.(in Chinese)

潘波,杨根庆,刘勇. 星点质心定位算法最优门限研究[J]. 光学精密工程,2008, 16(9):1787-1792. PAN B,YANG G Q,LIU Y. Study on optimization threshold of centroid algorithm[J]. Opt. Precison Eng., 2008, 16(9):1787-1792.(in Chinese)

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