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Original Articles

An approach for detection of buildings and changes in buildings using orthophotos and point clouds: A case study of Van Erriş earthquake

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Pages 627-642 | Received 11 Mar 2014, Accepted 18 Aug 2014, Published online: 17 Feb 2017

References

  • Adebowale A., Idowu S.A., Anyaehie A.A. (2013)—Comparative Study of Selected Data Mining Algorithms Used For Intrusion Detection. International Journal of Soft Computing and Engineering (IJSCE), 3 (3): 237–241.
  • Aytekin Ö., Erener A., Ulusoy I., Düzgün H.S.B. (2012)—Unsupervised building detection in complex urban environments from multispectral satellite imagery. International Journal of Remote Sensing, 33 (7): 2152–2177. doi: http://dx.doi.org/10.1080/01431161.2011.606852.
  • Baiocchi V., Brigante R., Dominici D., Giannone F., Radicioni F., Rosciano E. (2011)- Improving traditional change detection with DSM for update cartography in urbanized areas after seismic events. Remote sensing and geoinformation not only for scientific cooperation, Praha (CZ), 30th May—2nd June 2011, pp. 613–619—ISBN 9788001048689.
  • Baiocchi V., Brigante R., Dominici D., Milone M.V., Mormile M., Radicioni F. (2014a)- Automatic three-dimensional features extraction: The case study of L'Aquila for collapse identification after April 06, 2009 earthquake. European Journal of Remote Sensing, 47: 413–435. doi: http://dx.doi.org/10.5721/EuJRS20144724.
  • Baiocchi V., Dominici D., Milone M.V., Mormile M. (2014b)—Development of a software to optimize and plan the acquisitions from UAV and a first application in a post-seismic environment. European Journal of Remote Sensing, 47: 477–496. doi: http://dx.doi.org/10.5721/EuJRS20144727.
  • Baiocchi V., Brigante R., Radicioni F. (2010)—Three-dimensional multispectral classification and its applica-tion to early seismic damage assessment. Italian Journal of Remote Sensing, 42: 49–65. doi: http://dx.doi.org/10.5721/ItJRS20104234.
  • Boser B.E., Guyon I.M., Vapnik V.N. (1992)—A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, Pittsburgh, pp. 144–152.
  • Bruzzone L., Carlin L., Melgani F. (2004)—A multilevel hierarchical approach to classification of high spatial resolution images with support vector machines. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium proceedings: science for society: exploring and managing a changing planet, Piscataway (NJ), pp. 540–543. doi: http://dx.doi.org/10.1109/IGARSS.2004.1369083.
  • Burges C.J.C. (1998)—A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2: 121–167. doi: http://dx.doi.org/10.1023/A:1009715923555.
  • Chini M., Cinti F.R., Stramondo S. (2011)—Co-seismic surface effects from very high resolution panchromatic images: the case of the 2005 Kashmir (Pakistan) earthquake. Natural Hazards and Earth Systems Science, 11: 931–943. doi: http://dx.doi.org/10.5194/nhess-11-931-2011.
  • Coppin P., Jonckheere I., Nackaerts K., Muys B., Lambin E. (2004)—Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25 (9): 1565–1596. doi: http://dx.doi.org/10.1080/0143116031000101675.
  • Cortes C., Vapnik V. (1995)—Support-vector network, Machine Learning, 20: 273–297. doi: http://dx.doi.org/10.1007/BF00994018.
  • Cristianini N., Shawe-Taylor J. (2000)—An Introduction to Support Vector Machines and Other Kernelbased Learning Methods. Cambridge University Press. doi: http://dx.doi.org/10.1017/CBO9780511801389.
  • Dong L., Shan J. (2013)—A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 84: 85–99. doi: http://dx.doi.org/10.1016/jisprsjprs.2013.06.011.
  • ENVI Manual (2004)—Available at: http://aviris.gl.fcen.uba.ar/Curso_SR/biblio_sr/ENVI_userguid.pdf (last accessed 05.05.2014).
  • Erener A., Düzgün S., Yammer A.C. (2012)—Evaluating land use/cover change with temporal satellite data and information systems. Procedía Technology, 1: 385–389. doi: http://dx.doi.org/10.1016/j.protcy.2012.02.079.
  • Erener A., Yakar M. (2012)—Monitoring Coastline Change Using Remote Sensing and GIS Technologies. Lecture Notes in Information Technology, 30: 310–314.
  • Foody G.M., Boyd D.S., Sanchez-Hernandez C. (2007)—Mapping a specific class with an ensemble of classifiers. International Journal of Remote Sensing, 28 (8): 1733–1746. doi: http://dx.doi.org/10.1080/01431160600962566.
  • Foody G.M., Mathur A. (2006)—The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM. Remote Sensing of Environment, 103 (2): 179–189. doi: http://dx.doi.org/10.1016/j.rse.2006.04.001.
  • Gamba P., Casciati F. (1998)—GIS and image understanding for near-real-time earthquake damage assessment. Photogrammetric Engineering and Remote Sensing, 64: 987–994. doi: http://dx.doi.org/0099-1112/98/6410-987$3.00/0.
  • Giardina C.R., Dougherty E.R. (1988)—Morphological Methods in Image and Signal Processing Englewood Cliffs, NJ: Prentice-Hall.
  • Gonzalez R.C., Woods R.E., Eddins S.L. (2004)—Digital Image Processing Using Matlab. Pearson Education Incorporation, Upper Saddle River, New Jersey.
  • Huang C., Davis L.S., Townshend J.R.G. (2002)—An Assessment of Support Vector Machines for Land Cover Classification. International Journal of Remote Sensing, 23 (4): 725–749. doi: http://dx.doi.org/10.1080/01431160110040323.
  • Huang X., Zhang L., Li P. (2008)—A multiscale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform. International Journal of Remote Sensing, 29 (20): 5923–5941. doi: http://dx.doi.org/10.1080/01431160802139922.
  • Li H., Gu H., Han Y., Yang J. (2010)—Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine. International Journal of Remote Sensing, 31 (6): 1453–1470. doi: http://dx.doi.org/10.1080/01431160903475266.
  • Li Z., Gruen A. (2004)—Automatic DSM Generation from Linear Array Imagery Data. XXth ISPRS congress, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Istanbul, Turkey, pp. 128–133. ISBN: 3-906467-55-4.
  • Maas H.G., Vosselman G. (1999)—Two algorithms for extracting building models from raw laser altimetry data. ISPRS Journal of Photogrammetry and Remote Sensing, 54 (2–3): 153–163. doi: http://dx.doi.org/10.1016/S0924-2716(99)00004-0.
  • Malinverni E.S. (2011)—Change detection applying landscape metrics on high remote sensing images. Photogrammetric Engineering and Remote Sensing, 77 (10): 10451056. doi: http://dx.doi.org/10.14358/PERS.77.10.1045.
  • Peddabachigari S., Abraham A., Thomas J. (2004)—Intrusion Detection Systems Using Decision Trees and Support Vector Machines. International Journal of Applied Science and Computations, 11 (3): 118–134.
  • Sagale D., Kale A. (2014)—Combining Naive Bayesian and Support Vector Machinefor Intrusion Detection System. International Journal of Computing and Technology, 1 (3): 61–65.
  • Sarp G. (2012)—Determination of Vegetation Change Using Thematic Mapper Imagery in Afşin-Elbistan Lignite Basin; SE Turkey. Procedía Technology, 1: 407–411. doi: http://dx.doi.org/10.1016/j.protcy.2012.02.092.
  • Schenk T. (1999)—Digital Photogrammetry. Terra-Science, Laurelville, Ohio.
  • Sefercik U.G., Karakis S., Bayik C., Alkan M., Yastikli N. (2014)—Contribution of Normalized DSM to Automatic Building Extraction from HR Mono Optical Satellite Imagery. European Journal of Remote Sensing, 47: 575–591. doi: http://dx.doi.org/10.5721/EuJRS20144732.
  • Shufelt A.A., Mckeown D.M. (1993)—Fusion of Monocular Cues to Detect Man-Made Structures in Aerial Imagery. CVGIP: Image Understanding, 57 (3): 307–330. doi: http://dx.doi.org/10.1006/ciun.1993.1021.
  • Sonka M., Hlavac V., Boyle R. (1998)—Image Processing, Analysis, and Machine Vision. PWS Publishing.
  • Stramondo S., Bignami C., Chini M., Pierdicca N., Tertulliani A. (2006)—Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies. International Journal of Remote Sensing, 27 (20): 4433- 4447. doi: http://dx.doi.org/10.1080/01431160600675895.
  • Sumer E., Turker M. (2006)—An integrated earthquake damage detection system. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Salzburg, Austria, XXXVI, 4/C42.
  • Tuia D., Ratle F., Pozdnoukhov A., Camps-Valls G. (2010)—Multisource composite kernels for urban-image classification. IEEE Geoscience and Remote Sensing Letters, 7 (1): 88–92. doi: http://dx.doi.org/10.1109/LGRS.2009.2015341.
  • Turker M., San B.T. (2003)—SPOTHRV data analysis for detecting earthquake-induced changes in Izmit, Turkey. International Journal of Remote Sensing, 24 (12): 2439–2450. doi: http://dx.doi.org/10.1080/0143116031000070427.
  • Turker M., San B.T. (2004)—Detection of collapsed buildings caused by the 1999 Izmit, Turkey earthquake through digital analysis of post-event aerial photographs. International Journal of Remote Sensing, 25 (21): 4701–4714. doi: http://dx.doi.org/10.1080/0143116031000070427.
  • USGS (1998)—Standards for Digital Elevation Models. Fact Sheet 040–00 Retrieved on May 06, 2014, available at: http://nationalmap.gov/standards/demstds.html.
  • USGS (2011)—Bulletin for the 23 October 2011 magnitude 7.1 eastern Turkey earthquake. Available at: http://earthquake.usgs.gov/earthquakes/recenteqsww/Quakes/usb0006bqc.php.
  • Vapnik V.N. (1995)—The Nature of Statistical L earning Theory. New York: SpringerVerlag. doi: http://dx.doi.org/10.1007/978-1-4757-2440-0.
  • Vapnik V. N. (1998)—Statistical Learning Theory. New York: Wiley.
  • Voigt S., Kemper T., Riedlinger T., Kiefl R., Scholte K., Mehl H. (2007)—Satellite image analysis for disaster and crisis-management support. IEEE Transactions on Geoscience and Remote Sensing, 45 (6): 1520–1528. doi: http://dx.doi.org/10.1109/TGRS.2007.895830.
  • Yu H., Cheng G., Ge X. (2010)—Earthquake-collapsed building extraction from LiDAR and aero photograph based on OBIA. 2nd International Conference on Information Science and Engineering (ICISE), pp. 2034–2037.
  • Zhang J.F., Xie L.L., Tao X.X. (2003)—Change detection of remote sensing image for earthquake damaged buildings and its application in seismic disaster assessment. Proceedings of 2003 IEEE International Geoscience and Remote Sensing Symposium, 4: 2436–2438.