Abstract

Light detection and ranging (lidar) can record a 3D environment as point clouds, which are unstructured and difficult to process efficiently. Point cloud segmentation is an effective technology to solve this problem and plays a significant role in various applications, such as forestry management and 3D building reconstruction. The spectral information from images could improve the segmentation result, but suffers from the varying illumination conditions and the registration problem. New hyperspectral lidar sensor systems can solve these problems, with the capacity to obtain spectral and geometric information simultaneously. The former segmentation on hyperspectral lidar were mainly based on spectral information. The geometric segmentation method widely used by single wavelength lidar was not employed for hyperspectral lidar yet. This study aims to fill this gap by proposing a hyperspectral lidar segmentation method with three stages. First, Connected-Component Labeling (CCL) using the geometric information is employed for base segmentation. Second, the output components of the first stage are split by the spectral difference using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Third, the components of the second stage are merged based on the spectral similarity using Spectral Angle Match (SAM). Two indoor experimental scenes were setup for validation. We compared the performance of our mothed with that of the 3D and intensity feature based method. The quantitative analysis indicated that, our proposed method improved the point-weighted score by 19.35% and 18.65% in two experimental scenes, respectively. These results showed that the geometric segmentation method for single wavelength lidar could be combined with the spectral information, and contribute to the more effective hyperspectral lidar point cloud segmentation.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2019 (3)

2018 (7)

Q. Li and X. Cheng, “Comparison of different feature sets for tls point cloud classification,” Sensors (Basel) 18(12), 4206 (2018).
[Crossref] [PubMed]

S. Kaasalainen, M. Åkerblom, O. Nevalainen, T. Hakala, and M. Kaasalainen, “Uncertainty in multispectral lidar signals caused by incidence angle effects,” Interface Focus 8(2), 20170033 (2018).
[Crossref] [PubMed]

X. Ren, Y. Altmann, R. Tobin, A. Mccarthy, S. Mclaughlin, and G. S. Buller, “Wavelength-time coding for multispectral 3d imaging using single-photon lidar,” Opt. Express 26(23), 30146–30161 (2018).
[Crossref] [PubMed]

J. Sun, S. Shi, J. Yang, L. Du, W. Gong, B. Chen, and S. Song, “Analyzing the performance of prospect model inversion based on different spectral information for leaf biochemical properties retrieval,” ISPRS J. Photogramm. Remote Sens. 135, 74–83 (2018).
[Crossref]

B. C. Budei, B. St-Onge, C. Hopkinson, and F.-A. Audet, “Identifying the genus or species of individual trees using a three-wavelength airborne lidar system,” Remote Sens. Environ. 204, 632–647 (2018).
[Crossref]

L. Zhang, Z. Li, A. Li, and F. Liu, “Large-scale urban point cloud labeling and reconstruction,” ISPRS J. Photogramm. Remote Sens. 138, 86–100 (2018).
[Crossref]

Z. X. Pan, F. Y. Mao, W. Wang, T. Logan, and J. Hong, “Examining intrinsic aerosol-cloud interactions in south asia through multiple satellite observations,” J. Geophys. Res. Atmos. 123(19), 11210–11224 (2018).
[Crossref]

2017 (11)

E. Grilli, F. Menna, and F. Remondino, “A review of point clouds segmentation and classification algorithms,” ISPRS -. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci XLII-2(W3), 339–344 (2017).
[Crossref]

A. Börcs, B. Nagy, and C. Benedek, “Instant object detection in lidar point clouds,” IEEE Geosci. Remote Sens. Lett. 14(7), 992–996 (2017).
[Crossref]

M. Soilán, B. Riveiro, J. Martínez-Sánchez, and P. Arias, “Segmentation and classification of road markings using mls data,” ISPRS J. Photogramm. Remote Sens. 123, 94–103 (2017).
[Crossref]

C. Dong, L. Zhang, P. Takis, and X. Huang, “A methodology for automated segmentation and reconstruction of urban 3-d buildings from als point clouds,” IEEE J. Sel. Top. Appl. Earth Observ. 7, 4199–4217 (2017).

Z. Hui, B. Wu, Y. Hu, and Y. Y. Ziggah, “Improved progressive morphological filter for digital terrain model generation from airborne lidar data,” Appl. Opt. 56(34), 9359–9367 (2017).
[Crossref] [PubMed]

C. Dechesne, C. Mallet, A. L. Bris, and V. Gouet-Brunet, “Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery,” ISPRS J. Photogramm. Remote Sens. 126, 129–145 (2017).
[Crossref]

X. Yu, J. Hyyppä, P. Litkey, H. Kaartinen, M. Vastaranta, and M. Holopainen, “Single-sensor solution to tree species classification using multispectral airborne laser scanning,” Remote Sens. 9(2), 108 (2017).
[Crossref]

S. Morsy, A. Shaker, and A. El-Rabbany, “Multispectral lidar data for land cover classification of urban areas,” Sensors (Basel) 17(5), 958 (2017).
[Crossref] [PubMed]

S. Kaasalainen, L. Ruotsalainen, M. Kirkko-Jaakkola, O. Nevalainen, and T. Hakala, “Towards multispectral, multi-sensor indoor positioning and target identification,” Electron. Lett. 53(15), 1008–1011 (2017).
[Crossref]

B. Chen, S. Shi, W. Gong, Q. Zhang, J. Yang, L. Du, J. Sun, Z. Zhang, and S. Song, “Multispectral lidar point cloud classification: A two-step approach,” Remote Sens. 9(4), 373 (2017).
[Crossref]

L. Du, S. Shi, J. Yang, W. Wang, J. Sun, B. Cheng, Z. Zhang, and W. Gong, “Potential of spectral ratio indices derived from hyperspectral LiDAR and laser-induced chlorophyll fluorescence spectra on estimating rice leaf nitrogen contents,” Opt. Express 25(6), 6539–6549 (2017).
[Crossref] [PubMed]

2016 (8)

J. Fernandez-Diaz, W. Carter, C. Glennie, R. Shrestha, Z. Pan, N. Ekhtari, A. Singhania, D. Hauser, and M. Sartori, “Capability assessment and performance metrics for the titan multispectral mapping lidar,” Remote Sens. 8(11), 936 (2016).
[Crossref]

L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral lidar,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
[Crossref]

A. M. Ramiya, R. R. Nidamanuri, and R. Krishnan, “Object-oriented semantic labelling of spectral–spatial lidar point cloud for urban land cover classification and buildings detection,” Geocarto Int. 31(2), 121–139 (2016).
[Crossref]

K. Koenig and B. Höfle, “Full-waveform airborne laser scanning in vegetation studies—a review of point cloud and waveform features for tree species classification,” Forests 7(12), 198 (2016).
[Crossref]

X. Lu, J. Yao, J. Tu, K. Li, L. Li, and Y. Liu, “Pairwise linkage for point cloud segmentation,” Isprs Annals of Photogrammetry Remote Sensing & Spatial Informa III-3, 201–208 (2016).
[Crossref]

T. Hackel, J. D. Wegner, and K. Schindler, “Fast semantic segmentation of 3d point clouds with strongly varying density,” Isprs Annals of Photogrammetry Remote Sensing & Spatial Informa III-3, 177–184 (2016).
[Crossref]

M. Jarząbek-Rychard and A. Borkowski, “3d building reconstruction from als data using unambiguous decomposition into elementary structures,” ISPRS J. Photogramm. Remote Sens. 118, 1–12 (2016).
[Crossref]

H. Luo, C. Wang, C. Wen, Z. Cai, Z. Chen, H. Wang, Y. Yu, and J. Li, “Patch-based semantic labeling of road scene using colorized mobile lidar point clouds,” IEEE Trans. Intell. Transp. Syst. 17(5), 1286–1297 (2016).
[Crossref]

2015 (9)

W. Zhang, J. Zhao, M. Chen, Y. Chen, K. Yan, L. Li, J. Qi, X. Wang, J. Luo, and Q. Chu, “Registration of optical imagery and LiDAR data using an inherent geometrical constraint,” Opt. Express 23(6), 7694–7702 (2015).
[Crossref] [PubMed]

P. Duraisamy and B. Buckles, “Graph-connected components for filtering urban lidar data,” J. Appl. Remote Sens. 9(1), 096075 (2015).
[Crossref]

M. Awrangjeb, C. S. Fraser, and G. Lu, “Building change detection from lidar point cloud data based on connected component analysis,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2(W5), 393–400 (2015).
[Crossref]

S. Tao, F. Wu, Q. Guo, Y. Wang, W. Li, B. Xue, X. Hu, P. Li, D. Tian, C. Li, H. Yao, Y. Li, G. Xu, and J. Fang, “Segmenting tree crowns from terrestrial and mobile lidar data by exploring ecological theories,” ISPRS J. Photogramm. Remote Sens. 110, 66–76 (2015).
[Crossref]

Y. Qin, S. Li, T.-T. Vu, Z. Niu, and Y. Ban, “Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping,” Opt. Express 23(11), 13761–13775 (2015).
[Crossref] [PubMed]

A. V. Vo, L. Truong-Hong, D. F. Laefer, and M. Bertolotto, “Octree-based region growing for point cloud segmentation,” ISPRS J. Photogramm. Remote Sens. 104, 88–100 (2015).
[Crossref]

V. F. Strîmbu and B. M. Strîmbu, “A graph-based segmentation algorithm for tree crown extraction using airborne lidar data,” ISPRS J. Photogramm. Remote Sens. 104, 30–43 (2015).
[Crossref]

E. Puttonen, T. Hakala, O. Nevalainen, S. Kaasalainen, A. Krooks, M. Karjalainen, and K. Anttila, “Artificial target detection with a hyperspectral lidar over 26-h measurement artificial target detection with a hyperspectral lidar over 26-h measurement,” Opt. Eng. 54(1), 013105 (2015).
[Crossref]

S. Shuo, S. Shalei, G. Wei, D. Lin, Z. Bo, and H. Xin, “Improving backscatter intensity calibration for multispectral lidar,” IEEE Geosci. Remote Sens. Lett. 12(7), 1421–1425 (2015).
[Crossref]

2014 (2)

A. M. Wallace, A. Mccarthy, C. J. Nichol, X. Ren, S. Morak, D. Martinez-Ramirez, I. H. Woodhouse, and G. S. Buller, “Design and evaluation of multispectral lidar for the recovery of arboreal parameters,” IEEE Trans. Geosci. Remote Sens. 52(8), 4942–4954 (2014).
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D. Chen, L. Zhang, P. T. Mathiopoulos, and X. Huang, “A methodology for automated segmentation and reconstruction of urban 3-d buildings from als point clouds,” IEEE J. Sel. Top. Appl. Earth Observ. 7(10), 4199–4217 (2014).
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2013 (1)

J. Zhang, X. Lin, and X. Ning, “Svm-based classification of segmented airborne lidar point clouds in urban areas,” Remote Sens. 5(8), 3749–3775 (2013).
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2012 (1)

2011 (5)

D. Hoiem, A. N. Stein, A. A. Efros, and M. Hebert, “Recovering occlusion boundaries from an image,” Int. J. Comput. Vis. 91(3), 328–346 (2011).
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S. Kaasalainen, A. Jaakkola, M. Kaasalainen, A. Krooks, and A. Kukko, “Analysis of incidence angle and distance effects on terrestrial laser scanner intensity: Search for correction methods,” Remote Sens. 3(10), 2207–2221 (2011).
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K. K. Sareen, G. K. Knopf, and R. Canas, “Hierarchical data clustering approach for segmenting colored three-dimensional point clouds of building interiors,” Opt. Eng. 50(7), 077003 (2011).
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S. Song, W. Gong, B. Zhu, and X. Huang, “Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance,” ISPRS J. Photogramm. Remote Sens. 66(5), 672–682 (2011).
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I. H. Woodhouse, C. Nichol, P. Sinclair, J. Jack, F. Morsdorf, T. J. Malthus, and G. Patenaude, “A multispectral canopy lidar demonstrator project,” IEEE Geosci. Remote Sens. Lett. 8(5), 839–843 (2011).
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2010 (3)

R. Hesami, A. Babhadiashar, and R. Hosseinnezhad, “Range segmentation of large building exteriors: A hierarchical robust approach,” Comput. Vis. Image Underst. 114(4), 475–490 (2010).
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A. Sampath and J. Shan, “Segmentation and reconstruction of polyhedral building roofs from aerial lidar point clouds,” IEEE Trans. Geosci. Remote Sens. 48(3), 1554–1567 (2010).
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E. Puttonen, J. Suomalainen, T. Hakala, E. Räikkönen, H. Kaartinen, S. Kaasalainen, and P. Litkey, “Tree species classification from fused active hyperspectral reflectance and lidar measurements,” For. Ecol. Manage. 260(10), 1843–1852 (2010).
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2008 (1)

J. M. Biosca and J. L. Lerma, “Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods,” ISPRS J. Photogramm. Remote Sens. 63(1), 84–98 (2008).
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2007 (2)

A. Jagannathan and E. L. Miller, “Three-dimensional surface mesh segmentation using curvedness-based region growing approach,” IEEE Trans. Pattern Anal. Mach. Intell. 29(12), 2195–2204 (2007).
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2006 (1)

T. Rabbani, F. Van Den Heuvel, and G. Vosselmann, “Segmentation of point clouds using smoothness constraint,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 36, 248–253 (2006).

2005 (1)

B. An, S.-h. Chen, and W.-d. Yan, “Application of sam algorithm in multispectral image classification,” Chinese Journal of Stereology and Image Analysis 1, 55–60 (2005).

2004 (1)

G. Vosselman, B. G. Gorte, G. Sithole, and T. Rabbani, “Recognising structure in laser scanner point clouds,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 46, 33–38 (2004).

2002 (1)

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1992 (1)

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B. An, S.-h. Chen, and W.-d. Yan, “Application of sam algorithm in multispectral image classification,” Chinese Journal of Stereology and Image Analysis 1, 55–60 (2005).

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E. Puttonen, T. Hakala, O. Nevalainen, S. Kaasalainen, A. Krooks, M. Karjalainen, and K. Anttila, “Artificial target detection with a hyperspectral lidar over 26-h measurement artificial target detection with a hyperspectral lidar over 26-h measurement,” Opt. Eng. 54(1), 013105 (2015).
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Arias, P.

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Audet, F.-A.

B. C. Budei, B. St-Onge, C. Hopkinson, and F.-A. Audet, “Identifying the genus or species of individual trees using a three-wavelength airborne lidar system,” Remote Sens. Environ. 204, 632–647 (2018).
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Awrangjeb, M.

M. Awrangjeb, C. S. Fraser, and G. Lu, “Building change detection from lidar point cloud data based on connected component analysis,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2(W5), 393–400 (2015).
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Babhadiashar, A.

R. Hesami, A. Babhadiashar, and R. Hosseinnezhad, “Range segmentation of large building exteriors: A hierarchical robust approach,” Comput. Vis. Image Underst. 114(4), 475–490 (2010).
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D. H. Ballard, “Generalizing the hough transform to detect arbitrary shapes,” Pattern Recognit. 13(2), 111–122 (1981).
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P. J. Besl and R. C. Jain, “Segmentation through variable-order surface fitting,” IEEE Trans. Pattern Anal. Mach. Intell. 10(2), 167–192 (1988).
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J. M. Biosca and J. L. Lerma, “Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods,” ISPRS J. Photogramm. Remote Sens. 63(1), 84–98 (2008).
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P. Duraisamy and B. Buckles, “Graph-connected components for filtering urban lidar data,” J. Appl. Remote Sens. 9(1), 096075 (2015).
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B. C. Budei, B. St-Onge, C. Hopkinson, and F.-A. Audet, “Identifying the genus or species of individual trees using a three-wavelength airborne lidar system,” Remote Sens. Environ. 204, 632–647 (2018).
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Buller, G. S.

X. Ren, Y. Altmann, R. Tobin, A. Mccarthy, S. Mclaughlin, and G. S. Buller, “Wavelength-time coding for multispectral 3d imaging using single-photon lidar,” Opt. Express 26(23), 30146–30161 (2018).
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Cai, Z.

H. Luo, C. Wang, C. Wen, Z. Cai, Z. Chen, H. Wang, Y. Yu, and J. Li, “Patch-based semantic labeling of road scene using colorized mobile lidar point clouds,” IEEE Trans. Intell. Transp. Syst. 17(5), 1286–1297 (2016).
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K. K. Sareen, G. K. Knopf, and R. Canas, “Hierarchical data clustering approach for segmenting colored three-dimensional point clouds of building interiors,” Opt. Eng. 50(7), 077003 (2011).
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J. Fernandez-Diaz, W. Carter, C. Glennie, R. Shrestha, Z. Pan, N. Ekhtari, A. Singhania, D. Hauser, and M. Sartori, “Capability assessment and performance metrics for the titan multispectral mapping lidar,” Remote Sens. 8(11), 936 (2016).
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Chen, B.

J. Yang, Y. Cheng, L. Du, W. Gong, S. Shi, J. Sun, and B. Chen, “Analyzing the effect of the incidence angle on chlorophyll fluorescence intensity based on laser-induced fluorescence lidar,” Opt. Express 27(9), 12541–12550 (2019).
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J. Yang, L. Du, W. Gong, S. Shi, J. Sun, and B. Chen, “Analyzing the performance of the first-derivative fluorescence spectrum for estimating leaf nitrogen concentration,” Opt. Express 27(4), 3978–3990 (2019).
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J. Sun, S. Shi, J. Yang, L. Du, W. Gong, B. Chen, and S. Song, “Analyzing the performance of prospect model inversion based on different spectral information for leaf biochemical properties retrieval,” ISPRS J. Photogramm. Remote Sens. 135, 74–83 (2018).
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B. Chen, S. Shi, W. Gong, Q. Zhang, J. Yang, L. Du, J. Sun, Z. Zhang, and S. Song, “Multispectral lidar point cloud classification: A two-step approach,” Remote Sens. 9(4), 373 (2017).
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Chen, D.

D. Chen, L. Zhang, P. T. Mathiopoulos, and X. Huang, “A methodology for automated segmentation and reconstruction of urban 3-d buildings from als point clouds,” IEEE J. Sel. Top. Appl. Earth Observ. 7(10), 4199–4217 (2014).
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Chen, M.

Chen, S.-h.

B. An, S.-h. Chen, and W.-d. Yan, “Application of sam algorithm in multispectral image classification,” Chinese Journal of Stereology and Image Analysis 1, 55–60 (2005).

Chen, Y.

Chen, Z.

H. Luo, C. Wang, C. Wen, Z. Cai, Z. Chen, H. Wang, Y. Yu, and J. Li, “Patch-based semantic labeling of road scene using colorized mobile lidar point clouds,” IEEE Trans. Intell. Transp. Syst. 17(5), 1286–1297 (2016).
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Cheng, B.

Cheng, X.

Q. Li and X. Cheng, “Comparison of different feature sets for tls point cloud classification,” Sensors (Basel) 18(12), 4206 (2018).
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Cheng, Y.

Chu, Q.

Dechesne, C.

C. Dechesne, C. Mallet, A. L. Bris, and V. Gouet-Brunet, “Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery,” ISPRS J. Photogramm. Remote Sens. 126, 129–145 (2017).
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Dillencourt, M. B.

M. B. Dillencourt, H. Samet, and M. Tamminen, “A general approach to connected-component labeling for arbitrary image representations,” J. Assoc. Comput. Mach. 39(2), 253–280 (1992).
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Dong, C.

C. Dong, L. Zhang, P. Takis, and X. Huang, “A methodology for automated segmentation and reconstruction of urban 3-d buildings from als point clouds,” IEEE J. Sel. Top. Appl. Earth Observ. 7, 4199–4217 (2017).

Du, L.

J. Yang, L. Du, W. Gong, S. Shi, J. Sun, and B. Chen, “Analyzing the performance of the first-derivative fluorescence spectrum for estimating leaf nitrogen concentration,” Opt. Express 27(4), 3978–3990 (2019).
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J. Yang, Y. Cheng, L. Du, W. Gong, S. Shi, J. Sun, and B. Chen, “Analyzing the effect of the incidence angle on chlorophyll fluorescence intensity based on laser-induced fluorescence lidar,” Opt. Express 27(9), 12541–12550 (2019).
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J. Sun, S. Shi, J. Yang, L. Du, W. Gong, B. Chen, and S. Song, “Analyzing the performance of prospect model inversion based on different spectral information for leaf biochemical properties retrieval,” ISPRS J. Photogramm. Remote Sens. 135, 74–83 (2018).
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B. Chen, S. Shi, W. Gong, Q. Zhang, J. Yang, L. Du, J. Sun, Z. Zhang, and S. Song, “Multispectral lidar point cloud classification: A two-step approach,” Remote Sens. 9(4), 373 (2017).
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L. Du, S. Shi, J. Yang, W. Wang, J. Sun, B. Cheng, Z. Zhang, and W. Gong, “Potential of spectral ratio indices derived from hyperspectral LiDAR and laser-induced chlorophyll fluorescence spectra on estimating rice leaf nitrogen contents,” Opt. Express 25(6), 6539–6549 (2017).
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L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral lidar,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
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P. Duraisamy and B. Buckles, “Graph-connected components for filtering urban lidar data,” J. Appl. Remote Sens. 9(1), 096075 (2015).
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D. Hoiem, A. N. Stein, A. A. Efros, and M. Hebert, “Recovering occlusion boundaries from an image,” Int. J. Comput. Vis. 91(3), 328–346 (2011).
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Ekhtari, N.

J. Fernandez-Diaz, W. Carter, C. Glennie, R. Shrestha, Z. Pan, N. Ekhtari, A. Singhania, D. Hauser, and M. Sartori, “Capability assessment and performance metrics for the titan multispectral mapping lidar,” Remote Sens. 8(11), 936 (2016).
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S. Tao, F. Wu, Q. Guo, Y. Wang, W. Li, B. Xue, X. Hu, P. Li, D. Tian, C. Li, H. Yao, Y. Li, G. Xu, and J. Fang, “Segmenting tree crowns from terrestrial and mobile lidar data by exploring ecological theories,” ISPRS J. Photogramm. Remote Sens. 110, 66–76 (2015).
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J. Fernandez-Diaz, W. Carter, C. Glennie, R. Shrestha, Z. Pan, N. Ekhtari, A. Singhania, D. Hauser, and M. Sartori, “Capability assessment and performance metrics for the titan multispectral mapping lidar,” Remote Sens. 8(11), 936 (2016).
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M. A. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
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M. Awrangjeb, C. S. Fraser, and G. Lu, “Building change detection from lidar point cloud data based on connected component analysis,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2(W5), 393–400 (2015).
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J. Yang, Y. Cheng, L. Du, W. Gong, S. Shi, J. Sun, and B. Chen, “Analyzing the effect of the incidence angle on chlorophyll fluorescence intensity based on laser-induced fluorescence lidar,” Opt. Express 27(9), 12541–12550 (2019).
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B. Chen, S. Shi, W. Gong, Q. Zhang, J. Yang, L. Du, J. Sun, Z. Zhang, and S. Song, “Multispectral lidar point cloud classification: A two-step approach,” Remote Sens. 9(4), 373 (2017).
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L. Du, S. Shi, J. Yang, W. Wang, J. Sun, B. Cheng, Z. Zhang, and W. Gong, “Potential of spectral ratio indices derived from hyperspectral LiDAR and laser-induced chlorophyll fluorescence spectra on estimating rice leaf nitrogen contents,” Opt. Express 25(6), 6539–6549 (2017).
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L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral lidar,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
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S. Song, W. Gong, B. Zhu, and X. Huang, “Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance,” ISPRS J. Photogramm. Remote Sens. 66(5), 672–682 (2011).
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G. Vosselman, B. G. Gorte, G. Sithole, and T. Rabbani, “Recognising structure in laser scanner point clouds,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 46, 33–38 (2004).

Gouet-Brunet, V.

C. Dechesne, C. Mallet, A. L. Bris, and V. Gouet-Brunet, “Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery,” ISPRS J. Photogramm. Remote Sens. 126, 129–145 (2017).
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Hauser, D.

J. Fernandez-Diaz, W. Carter, C. Glennie, R. Shrestha, Z. Pan, N. Ekhtari, A. Singhania, D. Hauser, and M. Sartori, “Capability assessment and performance metrics for the titan multispectral mapping lidar,” Remote Sens. 8(11), 936 (2016).
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Hebert, M.

D. Hoiem, A. N. Stein, A. A. Efros, and M. Hebert, “Recovering occlusion boundaries from an image,” Int. J. Comput. Vis. 91(3), 328–346 (2011).
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Hesami, R.

R. Hesami, A. Babhadiashar, and R. Hosseinnezhad, “Range segmentation of large building exteriors: A hierarchical robust approach,” Comput. Vis. Image Underst. 114(4), 475–490 (2010).
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B. C. Budei, B. St-Onge, C. Hopkinson, and F.-A. Audet, “Identifying the genus or species of individual trees using a three-wavelength airborne lidar system,” Remote Sens. Environ. 204, 632–647 (2018).
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Hosseinnezhad, R.

R. Hesami, A. Babhadiashar, and R. Hosseinnezhad, “Range segmentation of large building exteriors: A hierarchical robust approach,” Comput. Vis. Image Underst. 114(4), 475–490 (2010).
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Hu, X.

S. Tao, F. Wu, Q. Guo, Y. Wang, W. Li, B. Xue, X. Hu, P. Li, D. Tian, C. Li, H. Yao, Y. Li, G. Xu, and J. Fang, “Segmenting tree crowns from terrestrial and mobile lidar data by exploring ecological theories,” ISPRS J. Photogramm. Remote Sens. 110, 66–76 (2015).
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Hu, Y.

Huang, X.

C. Dong, L. Zhang, P. Takis, and X. Huang, “A methodology for automated segmentation and reconstruction of urban 3-d buildings from als point clouds,” IEEE J. Sel. Top. Appl. Earth Observ. 7, 4199–4217 (2017).

D. Chen, L. Zhang, P. T. Mathiopoulos, and X. Huang, “A methodology for automated segmentation and reconstruction of urban 3-d buildings from als point clouds,” IEEE J. Sel. Top. Appl. Earth Observ. 7(10), 4199–4217 (2014).
[Crossref]

S. Song, W. Gong, B. Zhu, and X. Huang, “Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance,” ISPRS J. Photogramm. Remote Sens. 66(5), 672–682 (2011).
[Crossref]

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Int. J. Appl. Earth Obs. Geoinf. (1)

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Figures (9)

Fig. 1
Fig. 1 Illustration of the hyperspectral lidar system.
Fig. 2
Fig. 2 Two indoor experimental scenes with nine and seven materials.
Fig. 3
Fig. 3 Flowchart of the our proposed three-stage point cloud segmentation method for hyperspectral lidar.
Fig. 4
Fig. 4 Illustration of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) of the segmentation in a binary case. The blue and red ellipses represent the ground truth component and component recognized by the segmentation method.
Fig. 5
Fig. 5 Hyperspectral lidar point cloud presented in false color; the false color was assigned by the first three components of PCA from the 32-channel spectra.
Fig. 6
Fig. 6 Output components by the CCL using geometric information that are enclosed in orange boxes. The colors of components are assigned random, so different components may be in the same color.
Fig. 7
Fig. 7 Points in the four components (i.e., (a) Sansevieria Trifasciata plant and ceramic flowerpot, (b) Aloe Vera plant and its white ceramic flowerpot, (c) the orange and green part of ceramic, and (d) plastic flowerpot) are clustered by DBSCAN.
Fig. 8
Fig. 8 Final segmentation result based on our proposed three-stage segmentation method.
Fig. 9
Fig. 9 3D and intensity feature based segmentation result.

Tables (4)

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Table 1 Recall, precision, and F score of nine materials based on CCL in scene one

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Table 2 Recall, precision, and F score of seven materials based on CCL in scene two

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Table 3 Recall, precision and F score of nine materials of final result in scene one

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Table 4 Recall, precision, and F score of seven materials of final result in scene two

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

DC = k / (dis_max 2 × density)
θ = cos 1 i = 1 32 x i y i ( i = 1 32 x i 2 ) 1 / 2 ( i = 1 32 y i 2 ) 1 / 2 , θ [ 0 , π 2 ]
r e c a l l = T P / ( T P + F N ) ,
p r e c i s i o n = T P / ( T P + F P ) ,
F score = 2 × r e c a l l × p r e c i s i o n r e c a l l + p r e c i s i o n
s c o r e w e i g h t e d = 1 i | R i | i | R i | max j [ T P ij / ( T P ij + F P ij + F N ij ) ] ,
s c o r e un w e i g h t e d = i 1 N r max j [ T P ij / ( T P ij + F P ij + F N ij ) ]

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