Stellar image registration based on mutual information in object area

DU Si-liang; YANG Bo; WANG Mi; FANG Sheng-hui

doi:10.3788/OPE.20162402.0406

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Stellar image registration based on mutual information in object area

更新时间：2020-08-13

- Stellar image registration based on mutual information in object area
- Optics and Precision Engineering Vol. 24, Issue 2, Pages: 406-412(2016)
- 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：
- DOI：10.3788/OPE.20162402.0406
  CLC： TP391.4;TP751.1
- Received：06 November 2015，
  
  Revised：14 December 2015，
  
  Published：25 February 2016
- 稿件说明：
移动端阅览
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

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