LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine

IEEE Geoscience and Remote Sensing Letters

Volumn 14, Issue 5, 2017, Pages 659-663

  Ghamisi, Pedram ^a,b   Höfle, Bernhard ^c  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^c UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

Composite kernel support vector machine (SVM); extended multiextinction profile (EP); light detection and ranging (LiDAR)

Indexed keywords

CLASSIFICATION (OF INFORMATION); SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; CONTEXTUAL INFORMATION; CPU PROCESSING TIME; DIGITAL SURFACE MODELS; EXTINCTION PROFILES; LIDAR POINT CLOUD DATUM; LIGHT DETECTION AND RANGING; SPATIAL INFORMATIONS;

OPTICAL RADAR;

EID: 85014870346     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2017.2669304     Document Type: Article

References (19)

1. Q. Chen, "Airborne lidar data processing and information extraction," Photogramm. Eng. Remote Sens., vol. 73, no. 2, pp. 109-112, 2007.
2. M. Khodadadzadeh, J. Li, S. Prasad, and A. Plaza, "Fusion of hyperspectral and LiDAR remote sensing data using multiple feature learning," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2971-2983, Jun. 2015.
3. P. Ghamisi, B. Höfle, and X. X. Zhu, "Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2016, to be published.
4. J. A. Benediktsson and P. Ghamisi, Spectral-Spatial Classification of Hyperspectral Remote Sensing Images. Boston, MA, USA: Artech House, 2015.
5. P. Ghamisi, R. Souza, J. A. Benediktsson, X. X. Zhu, L. Rittner, and R. A. Lotufo, "Extinction profiles for the classification of remote sensing data," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 10, pp. 5631-5645, Oct. 2016.
6. P. Ghamisi, R. Souza, J. A. Benediktsson, L. Rittner, R. Lotufo, and X. X. Zhu, "Hyperspectral data classification using extended extinction profiles," IEEE Geosci. Remote Sens. Lett., vol. 13, no. 11, pp. 1641-1645, Nov. 2016.
7. C. Debes et al., "Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2405-2418, Jun. 2014.
8. B. Höfle, M. Hollaus, and J. Hagenauer, "Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne lidar data," ISPRS J. Photogramm. Remote Sens., vol. 67, pp. 134-147, Jan. 2012.
9. N. Pfeifer, G. Mandlburger, J. Otepka, and W. Karel, "OPALS-A framework for airborne laser scanning data analysis," Comput., Environ. Urban Syst., vol. 45, pp. 125-136, May 2014.
10. B. Höfle and N. Pfeifer, "Correction of laser scanning intensity data: Data and model-driven approaches," ISPRS J. Photogramm. Remote Sens., vol. 62, no. 6, pp. 415-433, 2007.
11. M. Hollaus, C. Aubrecht, B. Höfle, K. Steinnocher, and W. Wagner, "Roughness mapping on various vertical scales based on full-waveform airborne laser scanning data," Remote Sens., vol. 3, no. 3, pp. 503-523, 2011.
12. P. Ghamisi, M. D. Mura, and J. A. Benediktsson, "A survey on spectral-spatial classification techniques based on attribute profiles," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2335-2353, May 2015.
13. E. Carlinet and T. Géraud, "A comparative review of component tree computation algorithms," IEEE Trans. Image Process., vol. 23, no. 9, pp. 3885-3895, Sep. 2014.
14. R. Souza, L. Rittner, R. Lotufo, and R. Machado, "An array-based node-oriented max-tree representation," in Proc. ICIP, Sep. 2015, pp. 3620-3624.
15. T. M. Cover, "Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition," IEEE Trans. Electron. Comput., vol. EC-14, no. 3, pp. 326-334, Jun. 1965.
16. B. Schölkopf and A. J. Smola, Learning With Kernels. Cambridge, MA, USA: MIT Press, 2002.
17. B. Schölkopf and A. J. Smola, Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA, USA: MIT Press, 2002.
18. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining Knowl. Discovery, vol. 2, no. 2, pp. 121-167, 1998.
19. G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J. Vila-Frances, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 93-97, Jan. 2006.