Target region extraction and segmentation algorithm for reflective tomography Lidar image. Issue 4 (23rd November 2022)

• Record Type: Journal Article
• Authors: Zhang, Xinyuan; Han, Fei; Shen, Shiyang; Wang, Yicheng; Xu, Shilong; Dong, Xiao; Hu, Yihua
• Is Part Of: IET image processing. Volume 17:Issue 4(2023)
• Page Start: 1001
• Page End: 1009
• Publication Date: 2022-11-23
• Subjects: image segmentation -- Lidar -- reflective tomography -- target region extraction; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12691
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: 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%.