EVALUATING THE POTENTIAL OF MULTISPECTRAL AIRBORNE LIDAR FOR TOPOGRAPHIC MAPPING AND LAND COVER CLASSIFICATION

42 citations as recorded by crossref.

1. Multispectral LiDAR Point Cloud Segmentation for Land Cover Leveraging Semantic Fusion in Deep Learning Network K. Xiao et al. 10.3390/rs15010243
2. 3DMASC: Accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric lidar data M. Letard et al. 10.1016/j.isprsjprs.2023.11.022
3. Multimodal hyperspectral remote sensing: an overview and perspective Y. Gu et al. 10.1007/s11432-020-3084-1
4. Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data L. Huo & E. Lindberg 10.1080/01431161.2020.1800127
5. Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data L. Cao et al. 10.1016/j.rse.2016.03.012
6. Identifying the genus or species of individual trees using a three-wavelength airborne lidar system B. Budei et al. 10.1016/j.rse.2017.09.037
7. Classification of LiDAR Data Combined Octave Convolution With Capsule Network H. Wu et al. 10.1109/ACCESS.2020.2965278
8. Multispectral LiDAR Data Classification Method Based on an Improved PointNet++ Model 庄. 景 10.12677/GST.2024.121009
9. Combining single photon and multispectral airborne laser scanning for land cover classification L. Matikainen et al. 10.1016/j.isprsjprs.2020.04.021
10. Introducing Improved Transformer to Land Cover Classification Using Multispectral LiDAR Point Clouds Z. Zhang et al. 10.3390/rs14153808
11. Classification of Multispectral Airborne LiDAR Data Using Geometric and Radiometric Information S. Morsy et al. 10.3390/geomatics2030021
12. Feasibility of Multispectral Airborne Laser Scanning Data for Road Mapping K. Karila et al. 10.1109/LGRS.2016.2631261
13. Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada S. Morsy et al. 10.3390/app8030349
14. Automatic LOD0 classification of airborne LiDAR data in urban and non-urban areas J. Balado et al. 10.1080/22797254.2018.1522934
15. Multispectral LiDAR Point Cloud Classification Using SE-PointNet++ Z. Jing et al. 10.3390/rs13132516
16. Topografik harita üretim tekniklerine ilişkin yükseklik hassasiyetlerinin arazi örtüsü tipi bağlamında karşılaştırılması A. ALTUNEL & O. SAKICI 10.53516/ajfr.1371189
17. Review on Active and Passive Remote Sensing Techniques for Road Extraction J. Jia et al. 10.3390/rs13214235
18. MKANet: Multiscale Kernel Aggregation Network for Multispectral LiDAR 3-D Point Cloud Scene Recognition K. Xiao et al. 10.1109/JSEN.2024.3385432
19. Qualifying the LIDAR-Derived Intensity Image as an Infrared Band in NDWI-Based Shoreline Extraction A. Incekara et al. 10.1109/JSTARS.2018.2875792
20. Impacts of light detection and ranging (LiDAR) data organization and unit of analysis on land cover classification D. Beaulne et al. 10.1080/01431161.2020.1856961
21. Classification of UAV point clouds by random forest machine learning algorithm M. ZEYBEK 10.31127/tuje.669566
22. Multispectral Light Detection and Ranging Technology and Applications: A Review N. Takhtkeshha et al. 10.3390/s24051669
23. A Discriminative Tensor Representation Model for Feature Extraction and Classification of Multispectral LiDAR Data Q. Wang & Y. Gu 10.1109/TGRS.2019.2947081
24. Angular Reflectance of Leaves With a Dual-Wavelength Terrestrial Lidar and Its Implications for Leaf-Bark Separation and Leaf Moisture Estimation S. Hancock et al. 10.1109/TGRS.2017.2652140
25. Semantic Segmentation of LiDAR Points Clouds: Rasterization Beyond Digital Elevation Models F. Guiotte et al. 10.1109/LGRS.2019.2958858
26. Assessment of Accuracy in the Identification of Rock Formations from Aerial and Terrestrial Laser-Scanning Data V. Paleček & P. Kubíček 10.3390/ijgi7040142
27. Target Classification of Similar Spatial Characteristics in Complex Urban Areas by Using Multispectral LiDAR B. Luo et al. 10.3390/rs14010238
28. Multiscale Adjacency Matrix CNN: Learning on Multispectral LiDAR Point Cloud via Multiscale Local Graph Convolution J. Yang et al. 10.1109/JSTARS.2023.3335300
29. Classification of Airborne Multispectral Lidar Point Clouds for Land Cover Mapping N. Ekhtari et al. 10.1109/JSTARS.2018.2835483
30. A Novel Multi-camera Fusion Approach at Plant Scale: From 2D to 3D E. Correa et al. 10.1007/s42979-024-02849-7
31. A Comparative Land-Cover Classification Feature Study of Learning Algorithms: DBM, PCA, and RF Using Multispectral LiDAR Data S. Pan et al. 10.1109/JSTARS.2019.2899033
32. An encoder–decoder network for land cover classification using a fusion of aerial images and photogrammetric point clouds S. Majidi et al. 10.1080/00396265.2024.2338641
33. Toward utilizing multitemporal multispectral airborne laser scanning, Sentinel-2, and mobile laser scanning in map updating L. Matikainen et al. 10.1117/1.JRS.13.4.044504
34. Land Cover Classification with Multispectral LiDAR Based on Multi-Scale Spatial and Spectral Feature Selection S. Shi et al. 10.3390/rs13204118
35. Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating L. Matikainen et al. 10.1016/j.isprsjprs.2017.04.005
36. Multi-attribute smooth graph convolutional network for multispectral points classification Q. Wang et al. 10.1007/s11431-020-1871-8
37. Multispectral LiDAR Point Cloud Classification: A Two-Step Approach B. Chen et al. 10.3390/rs9040373
38. Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information? M. Weinmann & M. Weinmann 10.3390/rs10010002
39. The effect of seasonal variation on automated land cover mapping from multispectral airborne laser scanning data K. Karila et al. 10.1080/01431161.2018.1528023
40. A Deep Neural Network Using Double Self-Attention Mechanism for ALS Point Cloud Segmentation L. Yu et al. 10.1109/ACCESS.2022.3158438
41. Review of studies on tree species classification from remotely sensed data F. Fassnacht et al. 10.1016/j.rse.2016.08.013
42. Deep Spatial Graph Convolution Network with Adaptive Spectral Aggregated Residuals for Multispectral Point Cloud Classification Q. Wang et al. 10.3390/rs15184417