Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression
In this paper, we address the problem of vision-based satellite recognition and pose estimation, which is to recognize the satellite from multiviews and estimate the relative poses using imaging sensors. We propose a vision-based method to solve these two problems using Gaussian process regression (...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/5921246 |
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| author | Haopeng Zhang Cong Zhang Zhiguo Jiang Yuan Yao Gang Meng |
| author_facet | Haopeng Zhang Cong Zhang Zhiguo Jiang Yuan Yao Gang Meng |
| author_sort | Haopeng Zhang |
| collection | DOAJ |
| description | In this paper, we address the problem of vision-based satellite recognition and pose estimation, which is to recognize the satellite from multiviews and estimate the relative poses using imaging sensors. We propose a vision-based method to solve these two problems using Gaussian process regression (GPR). Assuming that the regression function mapping from the image (or feature) of the target satellite to its category or pose follows a Gaussian process (GP) properly parameterized by a mean function and a covariance function, the predictive equations can be easily obtained by a maximum-likelihood approach when training data are given. These explicit formulations can not only offer the category or estimated pose by the mean value of the predicted output but also give its uncertainty by the variance which makes the predicted result convincing and applicable in practice. Besides, we also introduce a manifold constraint to the output of the GPR model to improve its performance for satellite pose estimation. Extensive experiments are performed on two simulated image datasets containing satellite images of 1D and 2D pose variations, as well as different noises and lighting conditions. Experimental results validate the effectiveness and robustness of our approach. |
| format | Article |
| id | doaj-art-3634b993733243df9c91cd6ab38b443a |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-3634b993733243df9c91cd6ab38b443a2025-02-03T05:58:13ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742019-01-01201910.1155/2019/59212465921246Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process RegressionHaopeng Zhang0Cong Zhang1Zhiguo Jiang2Yuan Yao3Gang Meng4Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, ChinaImage Processing Center, School of Astronautics, Beihang University, Beijing 100191, ChinaImage Processing Center, School of Astronautics, Beihang University, Beijing 100191, ChinaImage Processing Center, School of Astronautics, Beihang University, Beijing 100191, ChinaBeijing Institute of Remote Sensing Information, Beijing 100192, ChinaIn this paper, we address the problem of vision-based satellite recognition and pose estimation, which is to recognize the satellite from multiviews and estimate the relative poses using imaging sensors. We propose a vision-based method to solve these two problems using Gaussian process regression (GPR). Assuming that the regression function mapping from the image (or feature) of the target satellite to its category or pose follows a Gaussian process (GP) properly parameterized by a mean function and a covariance function, the predictive equations can be easily obtained by a maximum-likelihood approach when training data are given. These explicit formulations can not only offer the category or estimated pose by the mean value of the predicted output but also give its uncertainty by the variance which makes the predicted result convincing and applicable in practice. Besides, we also introduce a manifold constraint to the output of the GPR model to improve its performance for satellite pose estimation. Extensive experiments are performed on two simulated image datasets containing satellite images of 1D and 2D pose variations, as well as different noises and lighting conditions. Experimental results validate the effectiveness and robustness of our approach.http://dx.doi.org/10.1155/2019/5921246 |
| spellingShingle | Haopeng Zhang Cong Zhang Zhiguo Jiang Yuan Yao Gang Meng Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression International Journal of Aerospace Engineering |
| title | Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression |
| title_full | Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression |
| title_fullStr | Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression |
| title_full_unstemmed | Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression |
| title_short | Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression |
| title_sort | vision based satellite recognition and pose estimation using gaussian process regression |
| url | http://dx.doi.org/10.1155/2019/5921246 |
| work_keys_str_mv | AT haopengzhang visionbasedsatelliterecognitionandposeestimationusinggaussianprocessregression AT congzhang visionbasedsatelliterecognitionandposeestimationusinggaussianprocessregression AT zhiguojiang visionbasedsatelliterecognitionandposeestimationusinggaussianprocessregression AT yuanyao visionbasedsatelliterecognitionandposeestimationusinggaussianprocessregression AT gangmeng visionbasedsatelliterecognitionandposeestimationusinggaussianprocessregression |