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Open AccessArticle
Spatial Feature-Based ISAR Image Registration for Space Targets
by
Lizhi Zhao
Lizhi Zhao
Lizhi Zhao received her B.E. degree from Hebei University of Technology in 2008 and her Ph.D. degree [...]
Lizhi Zhao received her B.E. degree from Hebei University of Technology in 2008 and her Ph.D. degree from Beijing Institute of Technology in 2015. In 2013, she was a joint training student at the University of Pisa. She is currently a lecturer at Minzu University of China, Beijing. Her research interests include radar imaging and radar signal processing.
1,
Junling Wang
Junling Wang
Junling Wang received his B.E. and M.S. degrees from China University of Petroleum, Qingdao, China, [...]
Junling Wang received his B.E. and M.S. degrees from China University of Petroleum, Qingdao, China, in 2005 and 2008, respectively, and his Ph.D. degree from Beijing Institute of Technology (BIT), in 2013. He was an exchange student in the Department of Signal Theory and Communications, Universitat Politecnica de Catalunya in 2010. Since 2013, he has been working with the School of Information and Electronics, BIT, Beijing, China, where he is currently an associate professor. His current research interests include satellite detection and imaging and radar signal processing.
2,*,
Jiaoyang Su
Jiaoyang Su
Jiaoyang Su was born in 1988. He received his B.E. degree from Minzu University of China in 2011 and [...]
Jiaoyang Su was born in 1988. He received his B.E. degree from Minzu University of China in 2011 and his M.S. degree from Beijing Institute of Technology in 2013. He is currently a laboratory instructor at Minzu University of China, Beijing. His research interests include computer architecture and signal processing.
1 and
Haoyue Luo
Haoyue Luo
Haoyue Luo received her M.E. degree from Beijing Institute of Technology in 2015. She is currently a [...]
Haoyue Luo received her M.E. degree from Beijing Institute of Technology in 2015. She is currently a senior engineer at the China Aerospace Science and Industry Corporation Second Research Institute, Institute No. 25 of the Second Academy, Beijing. Her research interests focus on radar electromagnetic simulation.
3
1
School of Information Engineering, Minzu University of China, Beijing 100081, China
2
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
3
China Aerospace Science and Industry Corp Second Research Institute, Institute NO.25 of the Second Academy, Beijing 100854, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(19), 3625; https://doi.org/10.3390/rs16193625 (registering DOI)
Submission received: 31 July 2024
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Revised: 26 September 2024
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Accepted: 26 September 2024
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Published: 28 September 2024
Abstract
Image registration is essential for applications requiring the joint processing of inverse synthetic aperture radar (ISAR) images, such as interferometric ISAR, image enhancement, and image fusion. Traditional image registration methods, developed for optical images, often perform poorly with ISAR images due to their differing imaging mechanisms. This paper introduces a novel spatial feature-based ISAR image registration method. The method encodes spatial information by utilizing the distances and angles between dominant scatterers to construct translation and rotation-invariant feature descriptors. These feature descriptors are then used for scatterer matching, while the coordinate transformation of matched scatterers is employed to estimate image registration parameters. To mitigate the glint effects of scatterers, the random sample consensus (RANSAC) algorithm is applied for parameter estimation. By extracting global spatial information, the constructed feature curves exhibit greater stability and reliability. Additionally, using multiple dominant scatterers ensures adaptability to low signal-to-noise (SNR) ratio conditions. The effectiveness of the method is validated through both simulated and natural ISAR image sequences. Comparative performance results with traditional image registration methods, such as the SIFT, SURF and SIFT+SURF algorithms, are also included.
Share and Cite
MDPI and ACS Style
Zhao, L.; Wang, J.; Su, J.; Luo, H.
Spatial Feature-Based ISAR Image Registration for Space Targets. Remote Sens. 2024, 16, 3625.
https://doi.org/10.3390/rs16193625
AMA Style
Zhao L, Wang J, Su J, Luo H.
Spatial Feature-Based ISAR Image Registration for Space Targets. Remote Sensing. 2024; 16(19):3625.
https://doi.org/10.3390/rs16193625
Chicago/Turabian Style
Zhao, Lizhi, Junling Wang, Jiaoyang Su, and Haoyue Luo.
2024. "Spatial Feature-Based ISAR Image Registration for Space Targets" Remote Sensing 16, no. 19: 3625.
https://doi.org/10.3390/rs16193625
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