The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues
Open accessAbstract
This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.
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Cites
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,070
Haralock, R. M., & Shapiro, L. G. (1991) · Addison-Wesley Longman Publishing Co., Inc. eBooks · Cited by 3,933
Airborne laser scanning—an introduction and overview
Wehr, A., & Lohr, U. (1999) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/s0924-2716(99)00011-8 · Cited by 1,414
Finite-element methods for active contour models and balloons for 2-D and 3-D images
Cohen, L. D., & Cohen, I. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.244675 · Cited by 1,378
DEM Generation from Laser Scanner Data Using Adaptive TIN Models
Axelsson, P. (2000) · ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences · Cited by 1,233
Determination of terrain models in wooded areas with airborne laser scanner data
Kraus, K., & Pfeifer, N. (1998) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/s0924-2716(98)00009-4 · Cited by 1,218
Machine contouring using minimum curvature
Briggs, I. C. (1974) · Geophysics · doi:10.1190/1.1440410 · Cited by 1,167
A progressive morphological filter for removing nonground measurements from airborne LIDAR data
Zhang, K., Chen, S.-C., Whitman, D., Shyu, M.-L., Yan, J., & Zhang, C. (2003) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2003.810682 · Cited by 1,026
Sithole, G., & Vosselman, G. (2004) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2004.05.004 · Cited by 977
A comparison between photogrammetry and laser scanning
Baltsavias, E. P. (1999) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/s0924-2716(99)00014-3 · Cited by 771
Cited by
Che, E., Jung, J., & Olsen, M. J. (2019) · Sensors · doi:10.3390/s19040810 · Cited by 253
3D Convolutional Neural Networks for landing zone detection from LiDAR
Maturana, D., & Scherer, S. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139679 · Cited by 203
The Use of LiDAR-Derived DEM in Flood Applications: A Review
Muhadi, N. A., Abdullah, A. F., Bejo, S. K., Mahadi, M. R., & Mijic, A. (2020) · Remote Sensing · doi:10.3390/rs12142308 · Cited by 172
Quantifying understory vegetation density using small-footprint airborne lidar
Campbell, M. J., Dennison, P. E., Hudak, A. T., Parham, L. M., & Butler, B. W. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.06.023 · Cited by 126
Campbell, M. J., Dennison, P. E., & Butler, B. W. (2017) · International Journal of Geographical Information Science · doi:10.1080/13658816.2016.1270453 · Cited by 35
Metadata
- Authors
- Meng, X., Currit, N., & Zhao, K.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2010-03-22
- Volume
- 2
- Issue
- 3
- Pages
- 833-860
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2014091167
- Cited by
- 573
- References
- 46
- FWCI
- 19.48
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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