Abstract

Urban land cover map is essential for urban planning, environmental studies and management. This paper aims to demonstrate the potential of geometric and radiometric features derived from LiDAR waveform and point cloud data in urban land cover mapping with both parametric and non-parametric classification algorithms. Small footprint LiDAR waveform data acquired by RIEGL LMS-Q560 in Zhangye city, China is used in this study. A LiDAR processing chain is applied to perform waveform decomposition, range determination and radiometric characterization. With the synergic utilization of geometric and radiometric features derived from LiDAR data, urban land cover classification is then conducted using the Maximum Likelihood Classification (MLC), Support Vector Machines (SVM) and random forest algorithms. The results suggest that the random forest classifier achieved the most accurate result with overall classification accuracy of 91.82% and the kappa coefficient of 0.88. The overall accuracies of MLC and SVM are 84.02, and 88.48, respectively. The study suggest that the synergic utilization of geometric and radiometric features derived from LiDAR data can be efficiently used for urban land cover mapping, the non-parametric random forest classifier is a promising approach for the various features with different physical meanings.

© 2015 Optical Society of America

Full Article  |  PDF Article
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References

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2015 (2)

W. Y. Yan, A. Shaker, and N. El-Ashmawy, “Urban land cover classification using airborne LiDAR data: a review,” Remote Sens. Environ. 158, 295–310 (2015).
[Crossref]

Y. Qin, W. Yao, T. T. Vu, S. Li, Z. Niu, and Y. Ban, “Characterizing Radiometric Attributes of Point Cloud Using a Normalized Reflective Factor derived from Small Footprint LiDAR WaveformRange determination for generating point clouds from airborne small footprint LiDAR waveforms,” IEEE J Sel Top Appl. 8(2), 740–749 (2015).

2014 (1)

W. Y. Yan and A. Shaker, “Radiometric correction and normalization of airborne LiDAR intensity data for improving land cover classification,” IEEE T Geosci Remote. 52(12), 7658–7673 (2014).
[Crossref]

2012 (5)

W. Y. Yan, A. Shaker, A. Habib, and A. P. Kersting, “Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction,” ISPRS J Photogramm. 67, 35–44 (2012).
[Crossref]

Y. Qin, T. T. Vu, and Y. Ban, “Toward an Optimal Algorithm for LiDAR Waveform Decomposition,” IEEE Geosci Remote 482–486(3), 9 (2012).

B. Höfle, M. Hollaus, and J. Hagenauer, “Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data,” ISPRS J Photogramm. 67, 134–147 (2012).
[Crossref]

T. Sasaki, I. Junichi, I. Keiko, M. Yukihiro, and K. Katsunori, “Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data,” Landsc Ecol Eng. 8(2), 157–171 (2012).
[Crossref]

Y. Qin, T. T. Vu, Y. Ban, and Z. Niu, “Range determination for generating point clouds from airborne small footprint LiDAR waveforms,” Opt. Express 20(23), 25935–25947 (2012).
[Crossref] [PubMed]

2011 (2)

C. Mallet, F. Bretar, M. Roux, U. Soergel, and C. Heipke, “Relevance assessment of full-waveform lidar data for urban area classification,” ISPRS J Photogramm. 66(6), 71–84 (2011).
[Crossref]

D. Tuia, V. Michele, C. Loris, K. Mikhail, and M. Jordi, “A survey of active learning algorithms for supervised remote sensing image classification,” IEEE J Sel Top Appl. 5(3), 606–617 (2011).
[Crossref]

2010 (1)

Y. Ban, H. Hu, and I. M. Rangel, “Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach,” Int. J. Remote Sens. 31(6), 1391–1410 (2010).
[Crossref]

2009 (2)

W. Zhou, G. Huang, A. Troy, and M. L. Cadenasso, “Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study,” Remote Sens. Environ. 113(8), 1769–1777 (2009).
[Crossref]

S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
[Crossref]

2008 (2)

M. Herold, P. Mayaux, C. Woodcock, A. Baccini, and C. Schmullius, “Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets,” Remote Sens. Environ. 112(5), 2538–2556 (2008).
[Crossref]

S. Antonarakis, R. Keith, and J. Brasington, “Object-based land cover classification using airborne LiDAR,” Remote Sens. Environ. 112(6), 2988–2998 (2008).
[Crossref]

2007 (1)

B. Höfle and N. Pfeifer, “Correction of laser scanning intensity data: Data and model-driven approaches,” ISPRS J Photogramm. 62(6), 415–433 (2007).
[Crossref]

2006 (5)

B. Jutzi and U. Stilla, “Range determination with waveform recording laser systems using a Wiener Filter,” ISPRS J Photogramm. 61(2), 95–107 (2006).
[Crossref]

R. Brennan and T. L. Webster, “Object-oriented land cover classification of lidar-derived surfaces,” Can. J. Rem. Sens. 32(2), 162–172 (2006).
[Crossref]

A. P. Carleer and W. Eeleonore, “Urban land cover multi‐level region‐based classification of VHR data by selecting relevant features,” Int. J. Remote Sens. 27(6), 1035–1051 (2006).
[Crossref]

J. Xiao, Y. Shen, J. Ge, R. Tateishi, C. Tang, Y. Liang, and Z. Huang, “Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing,” Landsc. Urban Plan. 75(1), 69–80 (2006).

M. Ali, “Roles and challenges of urban design,” J. Urban Des. 11(2), 173–193 (2006).
[Crossref]

2004 (3)

S. George and G. Vosselman, “Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds,” ISPRS J Photogramm. 59(1), 85–101 (2004).

L. Zhou, R. E. Dickinson, Y. Tian, J. Fang, Q. Li, R. K. Kaufmann, C. J. Tucker, and R. B. Myneni, “Evidence for a significant urbanization effect on climate in China,” Proc. Natl. Acad. Sci. U.S.A. 101(26), 9540–9544 (2004).
[Crossref] [PubMed]

F. Melgani and B. Lorenzo, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE T Geosci Remote. 42(8), 1778–1790 (2004).
[Crossref]

2003 (1)

E. Hodgson, J. R. Jensen, J. A. Tullis, K. D. Riordan, and C. M. Archer, “Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness,” Photogramm Eng Rem S 69(9), 973–980 (2003).
[Crossref]

2002 (2)

G. Zhu and B. Dan, “Classification using ASTER data and SVM algorithms; The case study of Beer Sheva, Israel,” Remote Sens. Environ. 80(2), 233–240 (2002).
[Crossref]

G. Foody, “Status of land cover classification accuracy assessment,” Remote Sens. Environ. 80(1), 185–201 (2002).
[Crossref]

2001 (1)

L. Breiman, “Random forests,” Mach. Learn. 45(1), 5–32 (2001).
[Crossref]

2000 (1)

M. Brown, L. Hugh, and G. Steve, “Linear spectral mixture models and support vector machines for remote sensing,” IEEE T Geosci Remote. 38(5), 2346–2360 (2000).
[Crossref]

1997 (1)

P. M. Vitousek, H. A. Mooney, J. Lubchenco, and J. M. Melillo, “Human domination of Earth's ecosystems,” Science 277(5325), 494–499 (1997).
[Crossref]

1995 (3)

M. K. Ridd, “Exploring a VIS (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities,” Int. J. Remote Sens. 16(12), 2165–2185 (1995).
[Crossref]

C. Cortes and V. Vladimir, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[Crossref]

P. Justin and R. Schowengerdt, “A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification,” IEEE T Geosci Remote. 33(4), 981–996 (1995).
[Crossref]

1994 (1)

L. Janssen and J. W. Frans, “Accuracy assessment of satellite derived land-cover data: a review,” Photogramm. Eng. Remote Sensing 60(4), 419–426 (1994).

1992 (1)

G. Foody, N. Campbell, M. Trodd, and F. Wood, “Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification,” Photogramm Eng Rem S 58(9), 1335–1341 (1992).

1986 (1)

R. Quinlan, “Induction of decision trees,” Mach. Learn. 1(1), 81–106 (1986).
[Crossref]

1980 (1)

A. Strahler, “The use of prior probabilities in maximum likelihood classification of remotely sensed data,” Remote Sens. Environ. 10(2), 135–163 (1980).
[Crossref]

Ahokas, E.

S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
[Crossref]

Akujarvi, A.

S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
[Crossref]

Ali, M.

M. Ali, “Roles and challenges of urban design,” J. Urban Des. 11(2), 173–193 (2006).
[Crossref]

Antonarakis, S.

S. Antonarakis, R. Keith, and J. Brasington, “Object-based land cover classification using airborne LiDAR,” Remote Sens. Environ. 112(6), 2988–2998 (2008).
[Crossref]

Archer, C. M.

E. Hodgson, J. R. Jensen, J. A. Tullis, K. D. Riordan, and C. M. Archer, “Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness,” Photogramm Eng Rem S 69(9), 973–980 (2003).
[Crossref]

Baccini, A.

M. Herold, P. Mayaux, C. Woodcock, A. Baccini, and C. Schmullius, “Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets,” Remote Sens. Environ. 112(5), 2538–2556 (2008).
[Crossref]

Ban, Y.

Y. Qin, W. Yao, T. T. Vu, S. Li, Z. Niu, and Y. Ban, “Characterizing Radiometric Attributes of Point Cloud Using a Normalized Reflective Factor derived from Small Footprint LiDAR WaveformRange determination for generating point clouds from airborne small footprint LiDAR waveforms,” IEEE J Sel Top Appl. 8(2), 740–749 (2015).

Y. Qin, T. T. Vu, Y. Ban, and Z. Niu, “Range determination for generating point clouds from airborne small footprint LiDAR waveforms,” Opt. Express 20(23), 25935–25947 (2012).
[Crossref] [PubMed]

Y. Qin, T. T. Vu, and Y. Ban, “Toward an Optimal Algorithm for LiDAR Waveform Decomposition,” IEEE Geosci Remote 482–486(3), 9 (2012).

Y. Ban, H. Hu, and I. M. Rangel, “Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach,” Int. J. Remote Sens. 31(6), 1391–1410 (2010).
[Crossref]

Brasington, J.

S. Antonarakis, R. Keith, and J. Brasington, “Object-based land cover classification using airborne LiDAR,” Remote Sens. Environ. 112(6), 2988–2998 (2008).
[Crossref]

Breiman, L.

L. Breiman, “Random forests,” Mach. Learn. 45(1), 5–32 (2001).
[Crossref]

Brennan, R.

R. Brennan and T. L. Webster, “Object-oriented land cover classification of lidar-derived surfaces,” Can. J. Rem. Sens. 32(2), 162–172 (2006).
[Crossref]

Bretar, F.

C. Mallet, F. Bretar, M. Roux, U. Soergel, and C. Heipke, “Relevance assessment of full-waveform lidar data for urban area classification,” ISPRS J Photogramm. 66(6), 71–84 (2011).
[Crossref]

Brown, M.

M. Brown, L. Hugh, and G. Steve, “Linear spectral mixture models and support vector machines for remote sensing,” IEEE T Geosci Remote. 38(5), 2346–2360 (2000).
[Crossref]

Cadenasso, M. L.

W. Zhou, G. Huang, A. Troy, and M. L. Cadenasso, “Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study,” Remote Sens. Environ. 113(8), 1769–1777 (2009).
[Crossref]

Campbell, N.

G. Foody, N. Campbell, M. Trodd, and F. Wood, “Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification,” Photogramm Eng Rem S 58(9), 1335–1341 (1992).

Carleer, A. P.

A. P. Carleer and W. Eeleonore, “Urban land cover multi‐level region‐based classification of VHR data by selecting relevant features,” Int. J. Remote Sens. 27(6), 1035–1051 (2006).
[Crossref]

Cortes, C.

C. Cortes and V. Vladimir, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[Crossref]

Dan, B.

G. Zhu and B. Dan, “Classification using ASTER data and SVM algorithms; The case study of Beer Sheva, Israel,” Remote Sens. Environ. 80(2), 233–240 (2002).
[Crossref]

Dickinson, R. E.

L. Zhou, R. E. Dickinson, Y. Tian, J. Fang, Q. Li, R. K. Kaufmann, C. J. Tucker, and R. B. Myneni, “Evidence for a significant urbanization effect on climate in China,” Proc. Natl. Acad. Sci. U.S.A. 101(26), 9540–9544 (2004).
[Crossref] [PubMed]

Eeleonore, W.

A. P. Carleer and W. Eeleonore, “Urban land cover multi‐level region‐based classification of VHR data by selecting relevant features,” Int. J. Remote Sens. 27(6), 1035–1051 (2006).
[Crossref]

El-Ashmawy, N.

W. Y. Yan, A. Shaker, and N. El-Ashmawy, “Urban land cover classification using airborne LiDAR data: a review,” Remote Sens. Environ. 158, 295–310 (2015).
[Crossref]

Fang, J.

L. Zhou, R. E. Dickinson, Y. Tian, J. Fang, Q. Li, R. K. Kaufmann, C. J. Tucker, and R. B. Myneni, “Evidence for a significant urbanization effect on climate in China,” Proc. Natl. Acad. Sci. U.S.A. 101(26), 9540–9544 (2004).
[Crossref] [PubMed]

Foody, G.

G. Foody, “Status of land cover classification accuracy assessment,” Remote Sens. Environ. 80(1), 185–201 (2002).
[Crossref]

G. Foody, N. Campbell, M. Trodd, and F. Wood, “Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification,” Photogramm Eng Rem S 58(9), 1335–1341 (1992).

Frans, J. W.

L. Janssen and J. W. Frans, “Accuracy assessment of satellite derived land-cover data: a review,” Photogramm. Eng. Remote Sensing 60(4), 419–426 (1994).

Ge, J.

J. Xiao, Y. Shen, J. Ge, R. Tateishi, C. Tang, Y. Liang, and Z. Huang, “Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing,” Landsc. Urban Plan. 75(1), 69–80 (2006).

George, S.

S. George and G. Vosselman, “Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds,” ISPRS J Photogramm. 59(1), 85–101 (2004).

Habib, A.

W. Y. Yan, A. Shaker, A. Habib, and A. P. Kersting, “Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction,” ISPRS J Photogramm. 67, 35–44 (2012).
[Crossref]

Hagenauer, J.

B. Höfle, M. Hollaus, and J. Hagenauer, “Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data,” ISPRS J Photogramm. 67, 134–147 (2012).
[Crossref]

Heipke, C.

C. Mallet, F. Bretar, M. Roux, U. Soergel, and C. Heipke, “Relevance assessment of full-waveform lidar data for urban area classification,” ISPRS J Photogramm. 66(6), 71–84 (2011).
[Crossref]

Herold, M.

M. Herold, P. Mayaux, C. Woodcock, A. Baccini, and C. Schmullius, “Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets,” Remote Sens. Environ. 112(5), 2538–2556 (2008).
[Crossref]

Hodgson, E.

E. Hodgson, J. R. Jensen, J. A. Tullis, K. D. Riordan, and C. M. Archer, “Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness,” Photogramm Eng Rem S 69(9), 973–980 (2003).
[Crossref]

Höfle, B.

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B. Höfle, M. Hollaus, and J. Hagenauer, “Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data,” ISPRS J Photogramm. 67, 134–147 (2012).
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W. Zhou, G. Huang, A. Troy, and M. L. Cadenasso, “Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study,” Remote Sens. Environ. 113(8), 1769–1777 (2009).
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J. Xiao, Y. Shen, J. Ge, R. Tateishi, C. Tang, Y. Liang, and Z. Huang, “Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing,” Landsc. Urban Plan. 75(1), 69–80 (2006).

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M. Brown, L. Hugh, and G. Steve, “Linear spectral mixture models and support vector machines for remote sensing,” IEEE T Geosci Remote. 38(5), 2346–2360 (2000).
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S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
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S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
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S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
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E. Hodgson, J. R. Jensen, J. A. Tullis, K. D. Riordan, and C. M. Archer, “Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness,” Photogramm Eng Rem S 69(9), 973–980 (2003).
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D. Tuia, V. Michele, C. Loris, K. Mikhail, and M. Jordi, “A survey of active learning algorithms for supervised remote sensing image classification,” IEEE J Sel Top Appl. 5(3), 606–617 (2011).
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T. Sasaki, I. Junichi, I. Keiko, M. Yukihiro, and K. Katsunori, “Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data,” Landsc Ecol Eng. 8(2), 157–171 (2012).
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P. Justin and R. Schowengerdt, “A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification,” IEEE T Geosci Remote. 33(4), 981–996 (1995).
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B. Jutzi and U. Stilla, “Range determination with waveform recording laser systems using a Wiener Filter,” ISPRS J Photogramm. 61(2), 95–107 (2006).
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S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
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S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
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T. Sasaki, I. Junichi, I. Keiko, M. Yukihiro, and K. Katsunori, “Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data,” Landsc Ecol Eng. 8(2), 157–171 (2012).
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S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
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Y. Qin, W. Yao, T. T. Vu, S. Li, Z. Niu, and Y. Ban, “Characterizing Radiometric Attributes of Point Cloud Using a Normalized Reflective Factor derived from Small Footprint LiDAR WaveformRange determination for generating point clouds from airborne small footprint LiDAR waveforms,” IEEE J Sel Top Appl. 8(2), 740–749 (2015).

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J. Xiao, Y. Shen, J. Ge, R. Tateishi, C. Tang, Y. Liang, and Z. Huang, “Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing,” Landsc. Urban Plan. 75(1), 69–80 (2006).

Litkey, P.

S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
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D. Tuia, V. Michele, C. Loris, K. Mikhail, and M. Jordi, “A survey of active learning algorithms for supervised remote sensing image classification,” IEEE J Sel Top Appl. 5(3), 606–617 (2011).
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Y. Qin, W. Yao, T. T. Vu, S. Li, Z. Niu, and Y. Ban, “Characterizing Radiometric Attributes of Point Cloud Using a Normalized Reflective Factor derived from Small Footprint LiDAR WaveformRange determination for generating point clouds from airborne small footprint LiDAR waveforms,” IEEE J Sel Top Appl. 8(2), 740–749 (2015).

Y. Qin, T. T. Vu, Y. Ban, and Z. Niu, “Range determination for generating point clouds from airborne small footprint LiDAR waveforms,” Opt. Express 20(23), 25935–25947 (2012).
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B. Höfle and N. Pfeifer, “Correction of laser scanning intensity data: Data and model-driven approaches,” ISPRS J Photogramm. 62(6), 415–433 (2007).
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S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
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Y. Qin, W. Yao, T. T. Vu, S. Li, Z. Niu, and Y. Ban, “Characterizing Radiometric Attributes of Point Cloud Using a Normalized Reflective Factor derived from Small Footprint LiDAR WaveformRange determination for generating point clouds from airborne small footprint LiDAR waveforms,” IEEE J Sel Top Appl. 8(2), 740–749 (2015).

Y. Qin, T. T. Vu, and Y. Ban, “Toward an Optimal Algorithm for LiDAR Waveform Decomposition,” IEEE Geosci Remote 482–486(3), 9 (2012).

Y. Qin, T. T. Vu, Y. Ban, and Z. Niu, “Range determination for generating point clouds from airborne small footprint LiDAR waveforms,” Opt. Express 20(23), 25935–25947 (2012).
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M. K. Ridd, “Exploring a VIS (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities,” Int. J. Remote Sens. 16(12), 2165–2185 (1995).
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E. Hodgson, J. R. Jensen, J. A. Tullis, K. D. Riordan, and C. M. Archer, “Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness,” Photogramm Eng Rem S 69(9), 973–980 (2003).
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C. Mallet, F. Bretar, M. Roux, U. Soergel, and C. Heipke, “Relevance assessment of full-waveform lidar data for urban area classification,” ISPRS J Photogramm. 66(6), 71–84 (2011).
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T. Sasaki, I. Junichi, I. Keiko, M. Yukihiro, and K. Katsunori, “Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data,” Landsc Ecol Eng. 8(2), 157–171 (2012).
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M. Herold, P. Mayaux, C. Woodcock, A. Baccini, and C. Schmullius, “Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets,” Remote Sens. Environ. 112(5), 2538–2556 (2008).
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P. Justin and R. Schowengerdt, “A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification,” IEEE T Geosci Remote. 33(4), 981–996 (1995).
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W. Y. Yan, A. Shaker, and N. El-Ashmawy, “Urban land cover classification using airborne LiDAR data: a review,” Remote Sens. Environ. 158, 295–310 (2015).
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W. Y. Yan and A. Shaker, “Radiometric correction and normalization of airborne LiDAR intensity data for improving land cover classification,” IEEE T Geosci Remote. 52(12), 7658–7673 (2014).
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W. Y. Yan, A. Shaker, A. Habib, and A. P. Kersting, “Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction,” ISPRS J Photogramm. 67, 35–44 (2012).
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J. Xiao, Y. Shen, J. Ge, R. Tateishi, C. Tang, Y. Liang, and Z. Huang, “Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing,” Landsc. Urban Plan. 75(1), 69–80 (2006).

Soergel, U.

C. Mallet, F. Bretar, M. Roux, U. Soergel, and C. Heipke, “Relevance assessment of full-waveform lidar data for urban area classification,” ISPRS J Photogramm. 66(6), 71–84 (2011).
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B. Jutzi and U. Stilla, “Range determination with waveform recording laser systems using a Wiener Filter,” ISPRS J Photogramm. 61(2), 95–107 (2006).
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J. Xiao, Y. Shen, J. Ge, R. Tateishi, C. Tang, Y. Liang, and Z. Huang, “Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing,” Landsc. Urban Plan. 75(1), 69–80 (2006).

Tateishi, R.

J. Xiao, Y. Shen, J. Ge, R. Tateishi, C. Tang, Y. Liang, and Z. Huang, “Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing,” Landsc. Urban Plan. 75(1), 69–80 (2006).

Tian, Y.

L. Zhou, R. E. Dickinson, Y. Tian, J. Fang, Q. Li, R. K. Kaufmann, C. J. Tucker, and R. B. Myneni, “Evidence for a significant urbanization effect on climate in China,” Proc. Natl. Acad. Sci. U.S.A. 101(26), 9540–9544 (2004).
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W. Zhou, G. Huang, A. Troy, and M. L. Cadenasso, “Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study,” Remote Sens. Environ. 113(8), 1769–1777 (2009).
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L. Zhou, R. E. Dickinson, Y. Tian, J. Fang, Q. Li, R. K. Kaufmann, C. J. Tucker, and R. B. Myneni, “Evidence for a significant urbanization effect on climate in China,” Proc. Natl. Acad. Sci. U.S.A. 101(26), 9540–9544 (2004).
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D. Tuia, V. Michele, C. Loris, K. Mikhail, and M. Jordi, “A survey of active learning algorithms for supervised remote sensing image classification,” IEEE J Sel Top Appl. 5(3), 606–617 (2011).
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Tullis, J. A.

E. Hodgson, J. R. Jensen, J. A. Tullis, K. D. Riordan, and C. M. Archer, “Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness,” Photogramm Eng Rem S 69(9), 973–980 (2003).
[Crossref]

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P. M. Vitousek, H. A. Mooney, J. Lubchenco, and J. M. Melillo, “Human domination of Earth's ecosystems,” Science 277(5325), 494–499 (1997).
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Y. Qin, W. Yao, T. T. Vu, S. Li, Z. Niu, and Y. Ban, “Characterizing Radiometric Attributes of Point Cloud Using a Normalized Reflective Factor derived from Small Footprint LiDAR WaveformRange determination for generating point clouds from airborne small footprint LiDAR waveforms,” IEEE J Sel Top Appl. 8(2), 740–749 (2015).

Y. Qin, T. T. Vu, Y. Ban, and Z. Niu, “Range determination for generating point clouds from airborne small footprint LiDAR waveforms,” Opt. Express 20(23), 25935–25947 (2012).
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Y. Qin, T. T. Vu, and Y. Ban, “Toward an Optimal Algorithm for LiDAR Waveform Decomposition,” IEEE Geosci Remote 482–486(3), 9 (2012).

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R. Brennan and T. L. Webster, “Object-oriented land cover classification of lidar-derived surfaces,” Can. J. Rem. Sens. 32(2), 162–172 (2006).
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G. Foody, N. Campbell, M. Trodd, and F. Wood, “Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification,” Photogramm Eng Rem S 58(9), 1335–1341 (1992).

Woodcock, C.

M. Herold, P. Mayaux, C. Woodcock, A. Baccini, and C. Schmullius, “Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets,” Remote Sens. Environ. 112(5), 2538–2556 (2008).
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J. Xiao, Y. Shen, J. Ge, R. Tateishi, C. Tang, Y. Liang, and Z. Huang, “Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing,” Landsc. Urban Plan. 75(1), 69–80 (2006).

Yan, W. Y.

W. Y. Yan, A. Shaker, and N. El-Ashmawy, “Urban land cover classification using airborne LiDAR data: a review,” Remote Sens. Environ. 158, 295–310 (2015).
[Crossref]

W. Y. Yan and A. Shaker, “Radiometric correction and normalization of airborne LiDAR intensity data for improving land cover classification,” IEEE T Geosci Remote. 52(12), 7658–7673 (2014).
[Crossref]

W. Y. Yan, A. Shaker, A. Habib, and A. P. Kersting, “Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction,” ISPRS J Photogramm. 67, 35–44 (2012).
[Crossref]

Yao, W.

Y. Qin, W. Yao, T. T. Vu, S. Li, Z. Niu, and Y. Ban, “Characterizing Radiometric Attributes of Point Cloud Using a Normalized Reflective Factor derived from Small Footprint LiDAR WaveformRange determination for generating point clouds from airborne small footprint LiDAR waveforms,” IEEE J Sel Top Appl. 8(2), 740–749 (2015).

Yukihiro, M.

T. Sasaki, I. Junichi, I. Keiko, M. Yukihiro, and K. Katsunori, “Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data,” Landsc Ecol Eng. 8(2), 157–171 (2012).
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L. Zhou, R. E. Dickinson, Y. Tian, J. Fang, Q. Li, R. K. Kaufmann, C. J. Tucker, and R. B. Myneni, “Evidence for a significant urbanization effect on climate in China,” Proc. Natl. Acad. Sci. U.S.A. 101(26), 9540–9544 (2004).
[Crossref] [PubMed]

Zhou, W.

W. Zhou, G. Huang, A. Troy, and M. L. Cadenasso, “Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study,” Remote Sens. Environ. 113(8), 1769–1777 (2009).
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Can. J. Rem. Sens. (1)

R. Brennan and T. L. Webster, “Object-oriented land cover classification of lidar-derived surfaces,” Can. J. Rem. Sens. 32(2), 162–172 (2006).
[Crossref]

IEEE Geosci Remote (1)

Y. Qin, T. T. Vu, and Y. Ban, “Toward an Optimal Algorithm for LiDAR Waveform Decomposition,” IEEE Geosci Remote 482–486(3), 9 (2012).

IEEE J Sel Top Appl. (2)

Y. Qin, W. Yao, T. T. Vu, S. Li, Z. Niu, and Y. Ban, “Characterizing Radiometric Attributes of Point Cloud Using a Normalized Reflective Factor derived from Small Footprint LiDAR WaveformRange determination for generating point clouds from airborne small footprint LiDAR waveforms,” IEEE J Sel Top Appl. 8(2), 740–749 (2015).

D. Tuia, V. Michele, C. Loris, K. Mikhail, and M. Jordi, “A survey of active learning algorithms for supervised remote sensing image classification,” IEEE J Sel Top Appl. 5(3), 606–617 (2011).
[Crossref]

IEEE T Geosci Remote. (5)

M. Brown, L. Hugh, and G. Steve, “Linear spectral mixture models and support vector machines for remote sensing,” IEEE T Geosci Remote. 38(5), 2346–2360 (2000).
[Crossref]

W. Y. Yan and A. Shaker, “Radiometric correction and normalization of airborne LiDAR intensity data for improving land cover classification,” IEEE T Geosci Remote. 52(12), 7658–7673 (2014).
[Crossref]

S. Kaasalainen, H. Hyyppa, A. Kukko, P. Litkey, E. Ahokas, J. Hyyppa, H. Lehner, A. Jaakkola, J. Suomalainen, A. Akujarvi, M. Kaasalainen, and U. Pyysalo, “Radiometric calibration of LIDAR intensity with commercially available reference targets,” IEEE T Geosci Remote. 47(2), 588–598 (2009).
[Crossref]

F. Melgani and B. Lorenzo, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE T Geosci Remote. 42(8), 1778–1790 (2004).
[Crossref]

P. Justin and R. Schowengerdt, “A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification,” IEEE T Geosci Remote. 33(4), 981–996 (1995).
[Crossref]

Int. J. Remote Sens. (3)

M. K. Ridd, “Exploring a VIS (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities,” Int. J. Remote Sens. 16(12), 2165–2185 (1995).
[Crossref]

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Figures (11)

Fig. 1
Fig. 1 Study area.
Fig. 2
Fig. 2 The workflow of LiDAR data based urban land cover classification.
Fig. 3
Fig. 3 An aerial image acquired over downtown of Zhangye city.
Fig. 4
Fig. 4 The raster image of ΔZ for the corresponding area in Fig. 3(1.0m spatial resolution) .
Fig. 5
Fig. 5 The raster image of σZ for the corresponding area in Fig. 3(1.0m spatial resolution).
Fig. 6
Fig. 6 The raster image of hi for the corresponding area in Fig. 3(1.0m spatial resolution).
Fig. 7
Fig. 7 The raster image of wi for the corresponding area in Fig. 3(1.0m spatial resolution).
Fig. 8
Fig. 8 The raster image of NRF for the corresponding area in Fig. 3(1.0m spatial resolution).
Fig. 9
Fig. 9 Land cover map obtained by MLC classifier for the corresponding area in Fig. 3 (1.0m spatial resolution).
Fig. 10
Fig. 10 Land cover map obtained by SVM classifier for the corresponding area in Fig. 3(1.0m spatial resolution).
Fig. 11
Fig. 11 Land cover map obtained by random forest algorithm for the corresponding area in Fig. 3(1.0m spatial resolution).

Tables (3)

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Table 1 Number of ROIs used in the classification

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Table 2 Summary of accuracy assessment

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Table 3 Confusion matrix for the MLC, SVM and random forest classifications

Equations (2)

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f( x i )= h i exp[ ( x i a i ) 2 w i 2 ]
G( x )= i=1 k f( x i )

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