Registration of DSM data and remote sensing image based on relative phase histogram

LIU Song-lin; SUN Gang; NIU Zhao-dong; ZHANG Jiang-wei; CHEN Zeng-ping

doi:10.3788/OPE.20142206.1696

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Registration of DSM data and remote sensing image based on relative phase histogram

更新时间：2020-08-13

- Registration of DSM data and remote sensing image based on relative phase histogram
- Optics and Precision Engineering Vol. 22, Issue 6, Pages: 1696-1705(2014)
- 作者机构：
  
  国防科技大学自动目标识别重点实验室,湖南长沙,410073
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142206.1696
  CLC： TP391;TP751
- Received：27 December 2013，
  
  Revised：20 February 2014，
  
  Published：25 June 2014
- 稿件说明：
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刘松林, 孙刚, 牛照东等. 基于相对相位直方图的数字表面模型数据与遥感图像配准[J]. 光学精密工程, 2014,22(6): 1696-1705

LIU Song-lin, SUN Gang, NIU Zhao-dong etc. Registration of DSM data and remote sensing image based on relative phase histogram[J]. Editorial Office of Optics and Precision Engineering, 2014,22(6): 1696-1705
刘松林, 孙刚, 牛照东等. 基于相对相位直方图的数字表面模型数据与遥感图像配准[J]. 光学精密工程, 2014,22(6): 1696-1705 DOI： 10.3788/OPE.20142206.1696.

LIU Song-lin, SUN Gang, NIU Zhao-dong etc. Registration of DSM data and remote sensing image based on relative phase histogram[J]. Editorial Office of Optics and Precision Engineering, 2014,22(6): 1696-1705 DOI： 10.3788/OPE.20142206.1696.

摘要

针对数字表面模型（DSM）数据与可见光遥感图像信息融合的实际需求，提出了一种基于一致点漂移算法（CPD）与相对相位直方图（RPH）的两级配准策略来实现上述数据与图像的自动配准。首先，利用Canny算子提取图像边缘，将边缘点作为CPD算法的输入，实现两幅图像的粗匹配，从而得到初始对应点集并估算尺度因子；然后，定义了一种鲁棒且具有旋转、平移不变性的区域变化信息描述子-RPH，其在粗匹配结果的保障下还可以实现尺度不变性；最后，根据尺度因子在两幅图像中分别定义圆环模板，并利用RPH测度完成DSM图像与可见光遥感图像精配准。实验结果显示，使用RPH测度进行精配准后，基于CPD算法的粗匹配结果得到了有效校正，在数据自身存在透视失真情况下，算法配准误差约为2 pixel，能够满足DSM数据与遥感图像信息融合的需求。

Abstract

According to the application requirement of information fusion for Digital Surface Model (DSM) data and visual remote sensing images

a two-level registration strategy based on Coherent Point Drift (CPD) algorithm and Relative Phase Histogram(RPH) was proposed to realize automatic registration of the data and images above. Firstly

Canny operator was used to extract edge points

and the extracted points were taken as inputs of CPT algorithm to implement a coarse matching and to obtain the initial matching points. Meanwhile

the scale factor was estimated. Then

the RPH

a robust descriptor characterized by invariance to image rotation and translation

was defined to represent the information of area changes. The RPH was also a scale-invariance under the support of coarse matching results. Finally

ring templates were defined in both images according to the scale factor and the fine registration of DSM data and remote sensing images was achieved by RPH measurement. Experimental results demonstrate that coarse matching results based on CPD are corrected efficiently after fine registration using RPH measurement. The registration error of proposed algorithm is only about two pixels even when images have perspective distortion. These data indicate that the method can satisfy the information fusion requirement of DSM data and visual remote sensing images.

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Keywords

references

穆超,余洁,许磊,等. 基于高分辨率遥感影像的DSM建筑物点的提取研究[J]. 武汉大学学报:信息科学版, 2009,34(4):414-417. MU CH, YU J, XU L, et al.. Research on extracting building points from the DSM data combining the high-resolution remote sensing image [J]. Geo. Infor. Science of Wuhan Univ., 2009, 34(4):414-417. (in Chinese)

武继广. 基于机载激光雷达和遥感影像融合的地物探测方法研究[J]. 首都师范大学学报:自然科学版, 2009, 30(4):6-11. WU J G. A method for detecting objects by fusing LIDAR data and images [J]. J. Capital Normal Univ.:Natural Science Edition, 2009,30(4):6-11.(in Chinese)

刘松林,李志军,牛照东,等. 基于多源数据融合的平面目标前视模板制备[J]. 激光与红外, 2013,43(2): 217-220. LIU S L, LI ZH J, NIU ZH D, et al.. Forward looking template preparation of flat target based on multi-sources data fusion[J]. Laser & Infrared, 2013,43(2):217-220. (in Chinese)

聂宏宾,侯晴宇,赵明,等. 基于似然函数EM迭代的红外与可见光图像配准[J]. 光学精密工程, 2011,19(3):657-663. NIE H B, HOU Q Y, ZHAO M, et al.. IR/visible image registration based on EM iteration of log-likelihood function [J]. Opt. Precision Eng., 2011,19(3):657-663. (in Chinese)

朱英宏,李俊山,杨威,等. 红外与可见光图像特征点边缘描述与匹配算法[J]. 计算机辅助设计与图形学学报, 2013,25(6):857-864. ZHU Y H, LI J SH, YANG W, et al.. A feature points description and matching algorithm for edge in IR /Visual images[J]. Journal of CAD & CG., 2013, 25(6): 857-864. (in Chinese)

廉蔺. 红外与可见光遥感图像自动配准算法研究 [D]. 长沙: 国防科学技术大学, 2012. LIAN L. Research on automatic registration algorithm for infrared and visible remote sensing images [D]. Changsha: National University of Defense Technology, 2012. (in Chinese)

QIN Y M,CAO ZH G,ZHUO W. An automatic DSM and remote sensing images registration scheme using template matching technique. MIPPR2011. Proc. of SPIE,8004:80040P.

高晶,吴育峰,吴昆,等. 基于角点检测的图像匹配算法[J]. 仪器仪表学报, 2013,34(8):1717-1725. GAO J, WU Y F, WU K, et al.. Image matching method based on corner detection [J]. Chinese J. Scientific Instrument, 2013,34(8):1717-1725. (in Chinese)

周志勇, 薛维琴, 郑健,等. 基于t分布混合模型的点集非刚性配准算法[J]. 光学精密工程,2013, 21(9):2405-2419. ZHOU ZH Y, XUE W Q, ZHENG J, et al.. Point set non-rigid registration using t-distribution mixture model [J]. Opt. Precision Eng., 2013,21(9):2405-2419. (in Chinese)

MYRONENKO A, SONG X. Point set registration: Coherent Point Drift [J]. IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32(12): 2262-2275.

HORAUD R, FORBES F, et al.. Rigid and articulated point registration with expectation conditional maximization [J]. IEEE Trans. Pattern Anal. Mach. Intell., 2011,33 (Compendex): 587-602.

赵键,孙即祥,周石琳,等. 基于全局最优的快速一致性点漂移算法[J]. 电子与信息学报, 2012,34(3): 509-516. ZHAO J, SUN J X, ZHOU SH L, et al.. Fast coherent point drift algorithm based on global optimization [J]. Journal of Electronics & Information Technology, 2012,34(3): 509-516. (in Chinese)

王鹏,高颖慧,王平,等. 基于传感器参数和改良CPD算法的红外与可见光图像点云配准[J]. 红外与毫米波学报, 2012,31(2):171-176. WANG P, GAO Y H, WANG P, et al.. Infrared and visual image point set registration based on sensor parameters and refined CPD algorithm[J]. J. Infrared Millim. Waves, 2012,31(2):171-176. (in Chinese)

BELONGIE S, MALIK J, PUZICHA J. Shape matching and object recognition using shape contexts [J]. IEEE Trans. Pattern Anal..Mach. Intell., 2002,24 (24):509-522.

吴福朝. 计算机视觉中的数学方法[M]. 北京: 科学出版社. 2008. WU F CH. Mathematical Methods in Computer Vision[M]. Beijing: Science Press, 2008. (in Chinese)

HE X CH, YUNG N H. Corner detector based on global and local curvature properties[J]. Optical Engineering, 2008, 47(5):057008.

AWRANGJEB M,LU G J. Robust image corner detection based on the chord-to-point distance accumulation technique [J]. IEEE Trans. on Multimedia, 2008, 10(6):1059-1072.

ZHANG X H,WANG H X, SMITH A W B, et al.. Corner detection based on gradient correlation matrices of planar curves[J]. Pattern Recognition, 2010, 43:1207-1223.

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