Research Progress of Laser Reflective Tomography Techniques

Yihua, 胡以华 Hu; Xinyuan, 张鑫源 Zhang; Shilong, 徐世龙 Xu; Nanxiang, 赵楠翔 Zhao; Liang, 石亮 Shi

doi:10.3788/cjl202148.0401002

“…Lidar reflective tomography (LRT) system is a system suitable for long-distance target detection and identification [1][2][3]. As aerospace technology thrives, the exploration of aerial and space targets has become a hot topic.…”

Section: Introductionmentioning

confidence: 99%

Hybrid regularization method for LRT image reconstruction under incomplete projections

Guo,

Zhihan,

Wang

et al. 2024

Preprint

0

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Lidar reflective tomography (LRT) system is able to achieve long-distance target detection and collect laser projection data for target reconstruction. In target detection with laser, the projection data is often incomplete or missing, where the traditional reconstruction algorithms are no longer effective. Therefore, this paper applies the regularization model to LRT image reconstruction and proposes a hybrid regularization method for higher LRT imaging performance, to prevent the over-fitting problem of the model and improve the speed and stability of the proposed method. Meanwhile, the edge and the structural characteristics of the reconstructed target with LRT is better enhanced by using the proposed hybrid regularization method. By conducting the LRT outfield experiment and using the collected LRT projection data, the validity of the proposed method in incomplete projection reconstruction is well proved.

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“…The lidar reflective tomography (LRT) system is a system suitable for long-distance target detection and identification [1][2][3]. As aerospace technology thrives, the exploration of aerial and space targets has become a hot topic.…”

Section: Introductionmentioning

confidence: 99%

Hybrid regularization method for LRT image reconstruction under incomplete projections

Guo,

Zhihan,

Jiang

et al. 2024

Preprint

0

View full text Add to dashboard Cite

Lidar reflective tomography (LRT) system is able to achieve long-distance target detection and collect laser projection data for target reconstruction. In target detection with laser, the projection data is often incomplete or missing, where the traditional reconstruction algorithms are no longer effective. Therefore, this paper applies the regularization model to LRT image reconstruction and proposes a hybrid regularization method for higher LRT imaging performance, to prevent the over-fitting problem of the model and improve the speed and stability of the proposed method. Meanwhile, the edge and the structural characteristics of the reconstructed target with LRT is better enhanced by using the proposed hybrid regularization method. By conducting the LRT outfield experiment and using the collected LRT projection data, the validity of the proposed method in incomplete projection reconstruction is well proved.

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Target region extraction and segmentation algorithm for reflective tomography Lidar image

Zhang

¹

,

Han

²

,

Shen

³

et al. 2022

IET Image Processing

0

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Reflective tomography Lidar is long‐range, high‐resolution imaging Lidar. Because the angular resolution is independent of detection range, it enjoys promising application prospects in imaging of small space targets, estimation of barycentre range of space debris, and many other fields. In practice, images generated by reflective tomography Lidar generally contain a large number of artefacts and noise that need to be removed to obtain the target profile. To improve the quality of the target profile, an algorithm is proposed for the extraction and segmentation of the target region in reflective tomography Lidar images. According to the experimental results, the algorithm can achieve better segmentation results than the traditional threshold segmentation algorithms. In particular, the algorithm can maintain good segmentation results for those images with noticeable ring artefacts, strip artefacts, and noise while avoiding under‐segmentation or over‐segmentation. It also guarantees the integrity of the target segmentation, preserves the outer contour and detailed structure information of the target as much as possible, and improves the accuracy of the target segmentation. Compared with conventional threshold segmentation algorithms, the algorithm improves the quality of image segmentation, and can improve the quality factor by more than 3%.

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Research Progress of Laser Reflective Tomography Techniques

Cited by 7 publications

References 0 publications

Hybrid regularization method for LRT image reconstruction under incomplete projections

Hybrid regularization method for LRT image reconstruction under incomplete projections

Hybrid regularization method for LRT image reconstruction under incomplete projections

Target region extraction and segmentation algorithm for reflective tomography Lidar image

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