AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration
Remote sensing image registration has been widely applied in military and civilian fields, such as target recognition, visual navigation and change detection. The dynamic changes in the sensing environment and sensors bring differences to feature point detection in amount and quality, which is still...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/22/5314 |
| _version_ | 1797457904749510656 |
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| author | Jian Yang Chen Li Xuelong Li |
| author_facet | Jian Yang Chen Li Xuelong Li |
| author_sort | Jian Yang |
| collection | DOAJ |
| description | Remote sensing image registration has been widely applied in military and civilian fields, such as target recognition, visual navigation and change detection. The dynamic changes in the sensing environment and sensors bring differences to feature point detection in amount and quality, which is still a common and intractable challenge for feature-based registration approaches. With such multiple perturbations, the extracted feature points representing the same physical location in space may have different location accuracy. Most existing matching methods focus on recovering the optimal feature correspondences while they ignore the diversities of different points in position, which easily brings the model into a bad local extrema, especially when existing with the outliers and noises. In this paper, we present a novel accuracy-aware registration model for remote sensing. A soft weighting is designed for each sample to preferentially select more reliable sample points. To better estimate the transformation between input images, an optimal sparse approximation is applied to approach the transformation by multiple iterations, which effectively reduces the computation complexity and also improves the accuracy of approximation. Experimental results show that the proposed method outperforms the state-of-the-art approaches in both matching accuracy and correct matches. |
| first_indexed | 2024-03-09T16:29:28Z |
| format | Article |
| id | doaj.art-70b6aa9836184b89b417c3585dda12c1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T16:29:28Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-70b6aa9836184b89b417c3585dda12c12023-11-24T15:04:21ZengMDPI AGRemote Sensing2072-42922023-11-011522531410.3390/rs15225314AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image RegistrationJian Yang0Chen Li1Xuelong Li2School of Computer Science and School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Computer Science and School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, ChinaKey Laboratory of Intelligent Interaction and Applications, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaRemote sensing image registration has been widely applied in military and civilian fields, such as target recognition, visual navigation and change detection. The dynamic changes in the sensing environment and sensors bring differences to feature point detection in amount and quality, which is still a common and intractable challenge for feature-based registration approaches. With such multiple perturbations, the extracted feature points representing the same physical location in space may have different location accuracy. Most existing matching methods focus on recovering the optimal feature correspondences while they ignore the diversities of different points in position, which easily brings the model into a bad local extrema, especially when existing with the outliers and noises. In this paper, we present a novel accuracy-aware registration model for remote sensing. A soft weighting is designed for each sample to preferentially select more reliable sample points. To better estimate the transformation between input images, an optimal sparse approximation is applied to approach the transformation by multiple iterations, which effectively reduces the computation complexity and also improves the accuracy of approximation. Experimental results show that the proposed method outperforms the state-of-the-art approaches in both matching accuracy and correct matches.https://www.mdpi.com/2072-4292/15/22/5314remote sensingimage registrationfeature correspondencessoft weightingsparse approximation |
| spellingShingle | Jian Yang Chen Li Xuelong Li AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration Remote Sensing remote sensing image registration feature correspondences soft weighting sparse approximation |
| title | AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration |
| title_full | AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration |
| title_fullStr | AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration |
| title_full_unstemmed | AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration |
| title_short | AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration |
| title_sort | aa lmm robust accuracy aware linear mixture model for remote sensing image registration |
| topic | remote sensing image registration feature correspondences soft weighting sparse approximation |
| url | https://www.mdpi.com/2072-4292/15/22/5314 |
| work_keys_str_mv | AT jianyang aalmmrobustaccuracyawarelinearmixturemodelforremotesensingimageregistration AT chenli aalmmrobustaccuracyawarelinearmixturemodelforremotesensingimageregistration AT xuelongli aalmmrobustaccuracyawarelinearmixturemodelforremotesensingimageregistration |