Extracting Linear Structures from LiDAR Data by M split Estimation and Different Segmentation Approaches.
Saved in:
| Title: | Extracting Linear Structures from LiDAR Data by M split Estimation and Different Segmentation Approaches. |
|---|---|
| Authors: | Wyszkowska, Patrycja1 (AUTHOR), Duchnowski, Robert1 (AUTHOR) robert.duchnowski@uwm.edu.pl |
| Source: | Remote Sensing. Aug2026, Vol. 18 Issue 15, p2488. 19p. |
| Subjects: | Data extraction, Geospatial data, Outliers (Statistics), Image segmentation, Estimation theory |
| Abstract: | Highlights: What are the main findings? Processing LiDAR data of non-basic shapes demands data segmentation; this paper proposes a new solution in that context, providing models with similar accuracy but that are much faster than existing methods, such as the conventional sliding window algorithm. The tests show that the proposed method is especially advisable for absolute Msplit estimation. The squared Msplit estimation is more sensitive to the choice of segmentation method, especially for data of lower accuracy. Msplit estimation outperforms classical estimation methods when data are contaminated with outliers. What are the implications of the main findings? Extracting linear structures from LiDAR data requires segmentation when the object is complex. This paper shows that the application of the absolute Msplit estimation and the newly proposed segmentation method provides accurate models and can be used for determining structures such as gable roofs, wall edges, or terrain profiles. The squared Msplit estimation is recommended for modeling simpler shaped objects and using the conventional sliding window algorithm. The absolute Msplit estimation together with the proposed segmentation algorithm is also recommended for processing low accuracy data or data disturbed by outliers. Raw LiDAR data requires processing to yield more informative, reliable, and accurate products. This paper concerns Msplit estimation, addresses the problem of extracting linear structures from LiDAR point clouds, and examines different approaches to data segmentation. The study results show that, in practice, processing the entire observation set is usually insufficient, ineffective, and hence inadvisable. The paper considers three approaches: segmentation into disjoint intervals, a sliding window algorithm, and a new solution that combines the two. Tests based on simulated LiDAR data show that the latter approach is a good alternative to the sliding window algorithm. The new segmentation method is at least twice as fast as the sliding window algorithm, and the results are often similarly accurate (especially for narrower windows). Tests on more complex shapes or based on real data indicate that the sliding window algorithm modification is especially recommended for use with the absolute Msplit estimation. As for the second basic Msplit estimation variant, namely the squared Msplit estimation, its accuracy often depends on the choice of segmentation method. The tests demonstrated that Msplit estimation may outperform classical methods, as confirmed by processing real LiDAR data. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? Processing LiDAR data of non-basic shapes demands data segmentation; this paper proposes a new solution in that context, providing models with similar accuracy but that are much faster than existing methods, such as the conventional sliding window algorithm. The tests show that the proposed method is especially advisable for absolute Msplit estimation. The squared Msplit estimation is more sensitive to the choice of segmentation method, especially for data of lower accuracy. Msplit estimation outperforms classical estimation methods when data are contaminated with outliers. What are the implications of the main findings? Extracting linear structures from LiDAR data requires segmentation when the object is complex. This paper shows that the application of the absolute Msplit estimation and the newly proposed segmentation method provides accurate models and can be used for determining structures such as gable roofs, wall edges, or terrain profiles. The squared Msplit estimation is recommended for modeling simpler shaped objects and using the conventional sliding window algorithm. The absolute Msplit estimation together with the proposed segmentation algorithm is also recommended for processing low accuracy data or data disturbed by outliers. Raw LiDAR data requires processing to yield more informative, reliable, and accurate products. This paper concerns Msplit estimation, addresses the problem of extracting linear structures from LiDAR point clouds, and examines different approaches to data segmentation. The study results show that, in practice, processing the entire observation set is usually insufficient, ineffective, and hence inadvisable. The paper considers three approaches: segmentation into disjoint intervals, a sliding window algorithm, and a new solution that combines the two. Tests based on simulated LiDAR data show that the latter approach is a good alternative to the sliding window algorithm. The new segmentation method is at least twice as fast as the sliding window algorithm, and the results are often similarly accurate (especially for narrower windows). Tests on more complex shapes or based on real data indicate that the sliding window algorithm modification is especially recommended for use with the absolute Msplit estimation. As for the second basic Msplit estimation variant, namely the squared Msplit estimation, its accuracy often depends on the choice of segmentation method. The tests demonstrated that Msplit estimation may outperform classical methods, as confirmed by processing real LiDAR data. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18152488 |
