Estimation of Seismic Vulnerability Levels of Urban Structures with Multisensor Remote Sensing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 9, Issue 5, 2016, Pages 1913-1936

  Geiß, Christian ^a,b   Jilge, Marianne ^a   Lakes, Tobia ^b   Taubenböck, Hannes ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

^b HUMBOLDT UNIVERSITY   (Germany)

Author keywords

Earthquakes; Istanbul; Landsat; machine learning; object based image analysis; RapidEye; seismic vulnerability assessment; support vector machines (SVM); TanDEM X

Indexed keywords

DATA HANDLING; EARTHQUAKES; REMOTE SENSING; RURAL AREAS; SEISMOLOGY; SUPPORT VECTOR MACHINES;

MEAN ABSOLUTE PERCENTAGE ERROR; MULTISENSOR REMOTE SENSING; OPERATIONAL REQUIREMENTS; SOFT-MARGIN SUPPORT VECTOR MACHINES; SPATIOTEMPORAL ANALYSIS; SUPPORT VECTOR REGRESSION (SVR); VERY HIGH SPATIAL RESOLUTIONS; VULNERABILITY ANALYSIS;

ESTIMATION;

EARTHQUAKE DAMAGE; HAZARD ASSESSMENT; IMAGE ANALYSIS; LANDSAT; MACHINE LEARNING; REMOTE SENSING; SEISMIC ZONE; SPATIAL RESOLUTION; STRUCTURAL RESPONSE; SUPPORT VECTOR MACHINE; TANDEM-X; URBAN AREA;

ISTANBUL [ISTANBUL (PRV)]; ISTANBUL [TURKEY]; TURKEY; ZEYTINBURNU;

EID: 84936161448     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2015.2442584     Document Type: Article

References (103)

1. R. Bilham, "The seismic future of cities," Bul. Earthquake Eng., vol. 7, pp. 839-887, 2009.
2. T. L. Holzer and J. C. Savage, "Global earthquake fatalities and population," Earthquake Spectra, vol. 29, no. 1, pp. 155-175, 2013.
3. B. E. Tucker, "Reducing earthquake risk," Science, vol. 341, no. 6150, pp. 1070-1072, 2013.
4. M. Erdik et al., "Earthquake risk assessment for Istanbul metropolitan area. Earthquake engineering and engineering vibration," vol. 2, no. 1, pp. 1-23, 2003.
5. M. Wieland, M. Pittore, S. Parolai, J. Zschau, B. Moldobekov, and U. Begaliev, "Estimating building inventory for rapid seismic vulnerability assessment: Towards an integrated approach based on multisource imaging," Soil Dyn. Earthquake Eng., vol. 36, pp. 70-83, 2012.
6. C. Geiß and H. Taubenböck, "Remote sensing contributing to assess earthquake risk: From a literature review towards a roadmap," Nat. Hazards, vol. 68, pp. 7-48, 2013.
7. I. Riedel, P. Guéguen, F. Dunand, and S. Cottaz, "Macroscale vulnerability assessment of cities using association rule learning," Seismol. Res. Lett., vol. 85, no. 2, pp. 295-305, 2014.
8. I. Riedel, P. Guéguen, M. Dalla Mura, E. Pathier, T. Leduc, and J. Chanussot, "Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods," Nat. Hazards, vol. 76, pp. 1111-1141, 2014, doi: 10.1007/s11069-014-1538-0.
9. H. Taubenböck et al., "Assessing building vulnerability using synergistically remote sensing and civil engineering," in Urban and Regional Data Management, A. Kreck, M. Rumor, S. Zlatanova, and E. Fendel, Eds. New York, NY, USA: Taylor & Francis, 2009, pp. 287-300.
10. B. Borzi et al., "Vulnerability study on a large industrial area using satellite remotely sensed images," Bull. Earthquake Eng., vol. 9, pp. 675-690, 2011.
11. G. Su et al., "An integrated method combining remote sensing data and local knowledge for the large-scale estimation of seismic loss risks to buildings in the context of rapid socioeconomic growth: A case study in Tangshan, China," Remote Sens., vol. 7, no. 3, pp. 2543-2601, 2015.
12. F. Borfecchia et al., "Active and passive remote sensing for supporting the evaluation of the urban seismic vulnerability," Ital. J. Remote Sens., vol. 42, pp. 129-141, 2009.
13. P. Matsuka, E. Pathier, P. Guéguen, and J. Chanussot, "Seismic vulnerability assessment using support vector machine classification for remote sensing and in-situ data," in Proc. 15th World Conf. Earthquake Eng., Lisbon, Portugal, Sep. 24-28, 2012 [Online]. Available: http://www.iitk.ac.in/nicee/wcee/article/WCEE2012-5147.pdf
14. P. Matsuka, J. Chanussot, E. Pathier, and P. Guéguen, "A support vector regression approach for building seismic vulnerability assessment and evaluation from remote sensing and in-situ data," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Munich, Germany, Jul. 22-27, 2012, pp. 7533-7536.
15. C. Geiß, H. Taubenböck, S. Tyagunov, A. Tisch, J. Post, and T. Lakes, "Assessment of seismic building vulnerability from space," Earthquake Spectra, vol. 30, no. 4, pp. 1553-1583, 2014.
16. C. Geiß et al., "Estimation of seismic buildings structural types using multi-sensor remote sensing and machine learning techniques," ISPRS J. Photogramm. Remote Sens., vol. 104, pp. 175-188, 2015.
17. E. Banzhaf and R. Höfer, "Monitoring urban structure types as spatial indicators with CIR aerial photographs for a more effective urban environmental management," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 1, no. 2, pp. 129-138, Jun. 2008.
18. U. Heiden, W. Heldens, S. Roessner, K. Segl, T. Esch, and A. Mueller, "Urban structure type characterization using hyperspectral remote sensing and height information," Landsc. Urban Plann., vol. 105, pp. 361-375, 2012.
19. S. Pauleit and F. Duhme, "Assessing the environmental performance of land cover types for urban planning," Landscape Urban Plann., vol. 52, pp. 1-20, 2000.
20. S. Niebergall, A. Loew, and W. Mauser, "Integrative assessment of informal settlements using VHR remote sensing data-The Delhi case study," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 3, no. 1, pp. 193-205, Sep. 2008.
21. M. Wurm, H. Taubenböck, A. Roth, and S. Dech, "Urban structuring using multisensoral remote sensing data: By the example of the German cities Cologne and Dresden," in Proc. Urban Remote Sens. Event, Shanghai, China, 2009, pp. 1-8.
22. I. Baud, M. Kuffer, K. Pfeffer, R. Sliuzas, and S. Karuppannan, "Understanding heterogeneity in metropolitan India: The added value of remote sensing data for analyzing sub-standard residential areas," Int. J. Appl. Earth Observ. Geoinf., vol. 12, pp. 359-374, 2010.
23. M. Bochow, K. Segl, and H. Kaufmann, "Automating the built-up process of feature-based fuzzy logic models for the identification of urban biotopes from hyperspectral remote sensing data," in Proc. Urban Remote Sens. Joint Event, Paris, France, 2007, pp. 1-8.
24. W. Heldens, T. Esch, and U. Heiden, "Supporting urban micro climate modelling with airborne hyperspectral data," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Munich, Germany, Jul. 23-27, 2012, pp. 1598-1601.
25. B. Bechtel and C. Daneke, "Classification of local climate zones based on multiple earth observation data," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 4, pp. 1191-1202, Aug. 2012.
26. M. Mueller, K. Segl, U. Heiden, and H. Kaufmann, "Potential of highresolution satellite data in the context of vulnerability of buildings," Nat. Hazards, vol. 38, pp. 247-258, 2006.
27. M. Pittore and M. Wieland, "Towards a rapid probabilistic seismic vulnerability assessment using satellite and ground-based remote sensing," Nat. Hazards, vol. 68, pp. 115-145, 2013.
28. R. Pal and K. Pal, "A review on image segmentation techniques," Pattern Recognit., vol. 26, no. 9, pp. 1277-1294, 1993.
29. T. Blaschke, "Object based image analysis for remote sensing," ISPRS J. Photogramm. Remote Sens., vol. 65, pp. 2-16, 2010.
30. C. H. Chen and P.-G. P. Ho, "Statistical pattern recognition in remote sensing," Pattern Recognit., vol. 41, no. 9, pp. 2731-2741, 2008.
31. G. Camps-Valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, "Advances in hyperspectral image classification: Earth monitoring with statistical learning methods," IEEE Signal Process. Mag., vol. 31, no. 1, pp. 45-54, Jan. 2014.
32. S. Steiniger, T. Lange, D. Burghardt, and R.Weibel, "An approach for the classification of urban building structures based on discriminant analysis techniques," Trans. GIS, vol. 12, no. 1, pp. 31-59, 2008.
33. G. Tyc, J. Tulip, D. Schulten, M. Krischke, andM. Oxfort, "The RapidEye mission design," Acta Astronaut., vol. 56, pp. 213-219, 2005.
34. G. Krieger et al., "TanDEM-X: A satellite formation for high-resolution SAR interferometry," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 11, pp. 3317-3341, Nov. 2007.
35. C. J. Tucker, "NASA's global orthorectified Landsat data set," Photogramm. Eng. Remote Sens., vol. 70, pp. 313-322, 2004.
36. TurkStat. (2014). Turkish Statistical Institute [Online]. Available: http://rapor.tuik.gov.tr/reports/rwservlet?adnksdb2&ENVID=adnksdb2Env& report=wa-buyukbelediye.RDF&p-il1=34&p-kod=2&p-yil=2013&p- dil=1&desformat=html, accessed on July 10, 2014.
37. E. Cakti, "Issues with the earthquake vulnerability of Istanbul," Nat. Hazards, vol. 68, no. 1, pp. 227-228, 2013.
38. M. Erdik, Y. A. Biro, T. Onur, K. Sesetyan, and G. Birgoren, "Assessment of earthquake hazard in Turkey and neighboring regions," Ann. Geofis., vol. 42, no. 6, pp. 1125-1138, 1999.
39. R. S. Stein, A. A. Barka, and J. H. Dieterich, "Progressive failure on the North Anatolian Fault since 1939 by earthquake stress triggering," Geophys. J. Int., vol. 128, pp. 594-604, 1997.
40. T. Parsons, "Recalculated probability of M = 7 earthquakes beneath the Sea of Marmara, Turkey," J. Geophys. Res., vol. 109, B05304, 2004 [Online]. Available: https://profile.usgs.gov/myscience/upload-folder/ci2010May25175655426802003JB002667.pdf
41. J. Tigges, T. Lakes, and P. Hostert, "Urban vegetation classification: Benefits of multitemporal RapidEye satellite data," Remote Sens. Environ., vol. 136, pp. 66-75, 2013.
42. M. Drusch et al., "Sentinel-2: ESA's optical high-resolution mission for GMES operational services," Remote Sens. Environ., vol. 120, pp. 25-36, 2012.
43. J. R. Irons, J. L. Dwyer, and J. A. Barsi, "The next Landsat satellite: The Landsat data continuity mission," Remote Sens. Environ., vol. 122, pp. 11-21, 2012.
44. H. Taubenböck, T. Esch, A. Felbier, M. Wiesner, A. Roth, and S. Dech, "Monitoring urbanization in mega cities from space," Remote Sens. Environ., vol. 117, pp. 162-176, 2012.
45. S. A. Freeman, "Review of the development of the capacity spectrum method," J. Earthquake Technol., vol. 41, no. 1, pp. 1-13, 2004.
46. FEMA, Rapid Visual Screening of Buildings for Potential Seismic Hazards. A Handbook, FEMA 154, 2nd ed. Redwood City, CA, USA: Applied Technology Council, 2002 [Online]. Available: http://www. unicef.org/education/files/VisualEvaluationFEMA-154.pdf
47. R. Richter, Atmospheric/Topographic Correction for Satellite Imagery, ATCOR-2/3 User Guide, Version 7.0 BETA, 2008 [Online]. Available: http://www.dlr.de/eoc/Portaldata/60/Resources/dokumente/5-tech-mod/atcor3-manual-2012.pdf
48. T. Esch et al., "Urban footprint processor-Fully automated processing chain generating settlement masks from global data of the TanDEM-X mission," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 6, pp. 1617-1621, Nov. 2013.
49. R. M. Haralick, S. R. Sternberg, and X. Zhuang, "Image analysis using mathematical morphology," IEEE Trans. Pattern Anal. Mach. Intell., vol. 9, no. 4, pp. 532-550, Jul. 1987.
50. C. Geiß, M. Wurm, M. Breunig, A. Felbier, and H. Taubenböck, "Normalization of TanDEM-X DSM data in urban environments with morphological filters," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4348-4362, Aug. 2015.
51. D. Shepard, "A two-dimensional interpolation function for irregularlyspaced data," in Proc. 23rd ACM Nat. Conf. (ACM'68), Aug. 27-29, 1968, pp. 517-524.
52. M. Herold, X. Liu, and K. Clarke, "Spatial metrics and image texture for mapping urban land use," Photogramm. Eng. Remote Sens., vol. 69, pp. 991-1001, 2003.
53. U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, "Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information," ISPRS J. Photogramm. Remote Sens., vol. 58, no. 3-4, pp. 239-258, 2004.
54. T. Esch, M. Thiel, M. Bock, A. Roth, and S. Dech, "Improvement of image segmentation accuracy based on multiscale optimization procedure," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3, pp. 463-467, Jul. 2008.
55. G. M. Espindola, G. Camara, I. A. Reis, L. S. Bins, and A. M. Monteiro, "Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation," Int. J. Remote Sens., vol. 27, no. 14, pp. 3035-3040, 2006.
56. T. Hengl, "Finding the right pixel size," Comput. Geosci., vol. 32, pp. 1283-1298, 2006.
57. T. R. Martha, N. Kerle, C. J. van Westen, V. Jetten, and K. V. Kumar, "Segment optimization and data-driven thresholding for knowledgebased landslide detection by object-based image analysis," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 12, pp. 4928-4943, Dec. 2011.
58. P. Lu, A. Stumpf, N. Kerle, and N. Casagli, "Object-oriented change detection for landslide rapid mapping," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 4, pp. 701-705, Jul. 2011.
59. R. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification," IEEE Trans. Syst. Man Cybern., vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
60. F. Pacifici, M. Chini, and W. Emery, "A neural network approach using multi-scale textural metrics from very high resolution panchromatic imagery for urban land-use classification," Remote Sens. Environ., vol. 113, no. 6, pp. 1276-1292, 2009.
61. J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, "Monitoring vegetation systems in the great plains with ERTS," in Proc. 3rd Earth Resour. Technol. Satell.-1 Symp., 1974, pp. 309-317.
62. L. Bruzzone and L. Carlin, "A multilevel context-based system for classification of very high spatial resolution images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 9, pp. 2587-2600, Sep. 2006.
63. B. Johnson and Z. Xie, "Classifying a high resolution image of an urban area using super-object information," ISPRS J. Photogramm. Remote Sens., vol. 83, pp. 40-49, 2013.
64. G. F. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Inf. Theory, vol. 14, no. 1, pp. 55-63, Jan. 1968.
65. I. Kononenko, "Estimating attributes: Analysis and extensions of relief," in Proc. Eur. Conf. Mach. Learn., 1994, pp. 171-182.
66. M. A. Hall, "Correlation-based feature selection for machine learning," Ph.D. dissertation, Dep. Comput. Sci., Univ. Waikato, Hamilton, New Zealand, 1999.
67. M. Robnik-Šikonja and I. Kononenko, "Theoretical and empirical analysis of ReliefF and RReliefF," Mach. Learn. J., vol. 53, no. 1-2, pp. 23-69, 2003.
68. V. N. Vapnik, The Nature of Statistical Learning Theory. New York, NY, USA: Springer, 1995.
69. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining Knowl. Discovery, vol. 2, pp. 121-167, 1998.
70. P. Leinenkugel, T. Esch, and C. Künzer, "Settlement detection and impervious surface estimation in the Mekong Delta using optical and SAR remote sensing data," Remote Sens. Environ., vol. 115, pp. 3007-3019, 2011.
71. M. Volpi, D. Tuia, F. Bovolo, M. Kanevski, and L. Bruzzone, "Supervised change detection in VHR images using contextual information and support vector machines," Int. J. Appl. Earth Observ. Geoinf., vol. 20, pp. 77-85, 2013.
72. V. N. Vapnik, Statistical Learning Theory. Hoboken, NJ, USA: Wiley, 1998.
73. C. C. Chang and C. J. Lin. (2001). LIBSVM: A Library for Support Vector Machines [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm, accessed on May 26, 2014.
74. B. Schölkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA, USA: MIT Press, 2002.
75. B. Schölkopf and A. J. Smola, "A tutorial on support vector regression," Statist. Comput., vol. 14, pp. 199-222, 2004.
76. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
77. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2005.
78. G. Camps-Valls and L. Bruzzone, Kernel Methods for Remote Sensing Data Analysis. Hoboken, NJ, USA: Wiley, 2009.
79. G. Mountrakis, J. Im, and C. Ogole, "Support vector machines in remote sensing: A review," ISPRS J. Photogramm. Remote Sens., vol. 66, pp. 247-259, 2011.
80. S. Salcedo-Sanz, J. L. Rojo-Alvarez, M. Martinez-Ramon, and G. Camps-Valls, "Support vector machines in engineering: An overview," Wiley Interdiscip. Rev. Data Mining Knowl. Discovery, vol. 4, no. 3, pp. 234-267, 2014.
81. J. Muñoz-Marí, F. Bovolo, L. Gomez-Chova, L. Bruzzone, and G. Camp-Valls, "Semisupervised one-class support vector machines for classification of remote sensing data," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 8, pp. 3188-3197, Aug. 2010.
82. M. Marconcini, D. Fernandez-Prieto, and T. Buchholz, "Targeted landcover classification," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 7, pp. 4173-4193, Jul. 2014.
83. G. M. Foody, "Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy," Photogramm. Eng. Remote Sens., vol. 70, pp. 627-633, 2004.
84. C. W. Hsu and C. J. Lin, "A comparison of methods for multi-class support vector machines," IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415-425, Mar. 2002.
85. C. W. Hsu, C. C. Chang, and C. J. Lin, "A practical guide to support vector classification," Dep. Comput. Sci., Nat. Taiwan Univ., Tech. Rep., 2010 [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
86. L. Bruzzone, M. Chi, and M. Marconcini, "A novel transductive SVM for semisupervised classification of remote-sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3363-3373, Nov. 2006.
87. D. Tuia, F. Ratle, F. Pacifici, M. F. Kanevski, and W. J. Emery, "Active learning methods for remote sensing image classification," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 7, pp. 2218-2232, Jul. 2009.
88. D. Tuia, L. Copa, M. Kanevski, and J. Munoz-Mari, "A survey of active learning algorithms for supervised remote sensing image classification," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 606-617, Jun. 2011.
89. A. Stumpf, N. Lachiche, J. P. Malet, N. Kerle, and A. Puissant, "Active learning in the spatial domain for remote sensing image classification," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2492-2507, May 2014.
90. E. Pasolli, F. Melgani, D. Tuia, F. Pacifici, and W. J. Emery, "SVM active learning approach for image classification using spatial information," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 4, pp. 2217-2233, Apr. 2014.
91. B. Demir, L. Minello, and L. Bruzzone, "Definition of effective training sets for supervised classification of remote sensing images by a novel cost-sensitive active learning method," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 2, pp. 1272-1284, Feb. 2014.
92. D. Tuia, E. Pasolli, and W. J. Emery, "Using active learning to adapt remote sensing image classifiers," Remote Sens. Environ., vol. 115, pp. 2232-2242, 2011.
93. F. Löw, P. Knöfel, and C. Conrad, "Analysis of uncertainty in multitemporal object-based classification," ISPRS J. Photogramm. Remote Sens., vol. 105, pp. 91-106, 2015.
94. M. Pittore, "Focus maps: A means of prioritizing data collection for efficient geo-risk management," Ann. Geophys., vol. 58, no. 1, S0107, 2015, doi: 10.4401/ag-6692.
95. B. Krawczyk, M. Wozniak, and B. Cyganek, "Clustering-based ensembles for one-class classification," Inf. Sci., vol. 264, pp. 182-195, 2014.
96. C. Desir, S. Bernard, C. Petitjean, and L. Heutte, "One class random forests," Pattern Recognit., vol. 46, no. 12, pp. 3490-3506, 2013.
97. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. Cambridge, MA, USA: MIT Press, 2006.
98. J. Verrelst et al., "Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and-3," Remote Sens. Environ., vol. 118, pp. 127-139, 2012.
99. M. Wieland and M. Pittore, "Performance evaluation of machine learning algorithms for urban pattern recognition from multi-spectral satellite images," Remote Sens., vol. 6, pp. 2912-2939, 2014.
100. C. Cortes and V. Vapnik, "Support vector networks," Mach. Learn., vol. 20, pp. 273-297, 1995.
101. B. Schölkopf, R. C. Williamson, A. Smola, and J. Shawe-Taylor, "Support vector method for novelty detection," in Proc. Adv. Neural Inf. Process. Syst., Denver, CO, USA, 1999, vol. 12, pp. 582-588.
102. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, "Estimating the support of a high dimensional distribution," Neural Comput., vol. 13, no. 7, pp. 1443-1471, 2001.
103. H. Drucker, C. J. C. Burges, L. Kaufman, A. J. Smola, and V. Vapnik, "Support vector regression machines," Adv. Neural Inf. Process. Syst., vol. 9, pp. 155-161, 1997.