Feature point detection for optical and SAR remote sensing images registration

被引：0
作者：
Wang L. [1 ]
Liang H. [1 ]
Wang Z. [2 ]
Xu R. [2 ]
Shi G. [1 ]
机构：
[1] College of Electro-Mechanical Engineering, Changchun University of Science and Technology, Changchun
[2] Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2022年 / 30卷 / 14期
关键词：
feature points detection; nonlinear radiation difference; optical and SAR images; phase congruency; speckle noise;
D O I：
10.37188/OPE.20223014.1738
中图分类号：
学科分类号：
摘要：
The influence of SAR speckle noise makes it difficult for the existing state-of-the art algorithms to guarantee the repeatability rate of feature points when extracting them from optical and SAR images owing to the nonlinear radiation differences between optical and SAR remote sensing images, which consequently reduce the matching performance. To address the above problems, a Harris feature point extraction algorithm based on phase congruency moment feature is proposed. Firstly, blocking strategy was used to divide the input image into several image blocks；secondly, phase congruency intermediate moments were defined；then, phase congruency multi-moment maps were calculated for each image block；and finally, a voting strategy was designed on the phase congruency multi-moment maps. The feature points that appeared more than half of the time on the multi-moment image were selected as the final feature points. In this study, the simulated optical and SAR images were used as experimental data, and three different feature point detection algorithms were selected for comparison with the proposed algorithm. Experimental results showed that the proposed algorithm can overcome the influence of nonlinear radiation differences between optical and SAR remote sensing images and the SAR speckle noise, improving the repeatability rate of feature points effectively. The registration results on the real optical and SAR images showed that, compared with the other three algorithms, the matching points increased by 23, 26, and 35 pairs and the root mean square error decreased by 12. 6%, 37. 2%, and 40. 8%, respectively. The performance of registration algorithm was improved effectively. © 2022 Chinese Academy of Sciences. All rights reserved.
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页码：1738 / 1748
页数：10
相关论文
共 23 条
[11]  
XIANG Y M, WANG F, YOU H J., OS-SIFT：a robust SIFT-like algorithm for high-resolution optical-to-SAR image registration in suburban areas ［J］, IEEE Transactions on Geoscience and Remote Sensing, 56, 6, pp. 3078-3090, (2018)
[12]  
ZHANG X T, WANG Y H, LIU H W., Robust optical and SAR image registration based on OS-SIFT and cascaded sample consensus［J］, IEEE Geoscience and Remote Sensing Letters, 19, pp. 1-5, (2022)
[13]  
FAN J W, WU Y, LI M, Et al., SAR and optical image registration using nonlinear diffusion and phase congruency structural descriptor［J］, IEEE Transactions on Geoscience and Remote Sensing, 56, 9, pp. 5368-5379, (2018)
[14]  
BAO W X, SANG S E, SHEN X F., Remote sensing image registration algorithm based on entropy constrained and KAZE feature extraction［J］, Opt. Precision Eng, 28, 8, pp. 1810-1819, (2020)
[15]  
KOVESI P., Image Features from Phase Congruency［J］, Viderei：Journal of Computer Vision Research, 1, 3, pp. 1-26, (1999)
[16]  
KOVESI P., Phase Congruency Detects Corners and Edges［C］, The Australian Pattern recognition society conference, pp. 309-318, (2003)
[17]  
SUN M C, MA T X, SONG Y M, Et al., Automatic registration of optical and SAR remote sensing image based on phase feature［J］, Opt. Precision Eng, 29, 3, pp. 616-627, (2021)
[18]  
PAUL S, PATI U C., Automatic optical-to-SAR image registration using a structural descriptor［J］, IET Image Processing, 14, 1, pp. 62-73, (2020)
[19]  
LI J, HU Q, AI M., RIFT：multi-modal image matching based on radiation-variation insensitive feature transform［J］, IEEE Transactions on Image Processing：a Publication of the IEEE Signal Processing Society, (2019)
[20]  
MORRONE M C, OWENS R A., Feature detection from local energy［J］, Pattern Recognition Letters, 6, 5, pp. 303-313, (1987)
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