723
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

Discrimination of residential and industrial buildings using LiDAR data and an effective spatial-neighbor algorithm in a typical urban industrial park

, , , , , & show all
Pages 1-15 | Received 11 Jan 2014, Accepted 08 Jan 2015, Published online: 17 Feb 2017

References

  • Aguilar M.A., Saldana M.M., Aguilar F.J. (2013)—Geoeye-1 and worldview-2 pan-sharpened imagery for object-based classification in urban environments. International Journal of Remote Sensing, 34 (7): 2583–2606. doi: http://dx.doi.org/10.1080/01431161.2012.747018.
  • Allain C., Cloitre M. (1991)—Characterizing the lacunarity of random and deterministic fractal sets. Physical Review A, 44 (6): 3552–3558. doi: http://dx.doi.org/10.1103/PhysRevA.44.3552.
  • Axelsson P. (2000)—Dem generation from laser scanner data using adaptive tin models. International Archives of Photogrammetry and Remote Sensing, 33: 111–118 (B4/1; Part 4).
  • Chen Y., Su W., Li J., Sun Z. (2009)—Hierarchical object oriented classification using very high resolution imagery and lidar data over urban areas. Advances in Space Research, 43 (7): 1101–1110. doi: http://dx.doi.org/10.1016/j.asr.2008.11.008.
  • Congalton R.G., Oderwald R.G., Mead R.A. (1983)—Assessing landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing, 49 (12): 1671–1678.
  • Congalton R.G. (1991)—A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of environment, 37 (1): 35–46. doi: http://dx.doi.org/10.1016/0034-4257(91)90048-B.
  • Dong P. (2009)—Lacunarity analysis of raster datasets and 1d, 2d, and 3d point patterns. Computers & Geosciences, 35 (10): 2100–2110. doi: http://dx.doi.org/10.1016/j.cageo.2009.04.001.
  • Dorninger P., Pfeifer N. (2008)—A comprehensive automated 3d approach for building extraction, reconstruction, and regularization from airborne laser scanning point clouds. Sensors, 8 (11): 7323–7343. doi: http://dx.doi.org/10.3390/s8117323.
  • Forzieri G., Tanteri L., Moser G., Catani F. (2013)—Mapping natural and urban environments using airborne multi-sensor ads40-mivis-lidar synergies. International Journal of Applied Earth Observation and Geoinformation, 23: 313–323. doi: http://dx.doi.org/10.1016/j.jag.2012.10.004.
  • Guan H., Ji Z., Zhong L., Li J., Ren Q. (2013 a)—Partially supervisedhierarchical classification for urban features from lidar data with aerial imagery. International Journal of Remote Sensing, 34 (1): 190–210. doi: http://dx.doi.org/10.1080/01431161.2012.712228.
  • Guan H., Li J., Chapman M., Deng F., Ji Z., Yang X. (2013b)—Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests. International Journal of Remote Sensing, 34 (14): 5166–5186. doi: http://dx.doi.org/10.1080/01431161.2013.788261.
  • Guo L., Chehata N., Mallet C., Boukir S. (2011)—Relevance of airborne lidar and multispectral image data for urban scene classification using random forests. Isprs Journal of Photogrammetry and Remote Sensing, 66 (1): 56–66. doi: http://dx.doi.org/10.1016/j.isprsjprs.2010.08.007.
  • Han N., Wu J., Tahmassebi A.R.S., Xu H.-W., Wang K. (2011)—Ndvi-based lacunarity texture for improving identification of torreya using object-oriented method. Agricultural Sciences in China, 10 (9): 1431–1444. doi: http://dx.doi.org/10.1016/S1671-2927(11)60136-3.
  • Kit O., Lüdeke M., Reckien D. (2012)—Texture-based identification of urban slums in hyderabad, india using remote sensing data. Applied Geography, 32 (2): 660–667. doi: http://dx.doi.org/10.1016/j.apgeog.2011.07.016.
  • Kitada K., Fukuyama K. (2012)—Land-use and land-cover mapping using a gradable classification method. Remote Sensing, 4 (6): 1544–1558. doi: http://dx.doi.org/10.3390/rs4061544.
  • Li M., Zang S.Y., Zhang B., Li S.S., Wu C.S. (2014)—A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47: 389–411. doi: http://dx.doi.org/10.5721/EuJRS20144723.
  • Malhi Y., Romàn-Cuesta R.M. (2008)—Analysis of lacunarity and scales of spatial homogeneity in ikonos images of amazonian tropical forest canopies. Remote Sensing of Environment, 112 (5): 2074–2087. doi: http://dx.doi.org/10.1016/j.rse.2008.01.009.
  • Mandelbrot B.B. (1983)—The fractal geometry of nature. Macmillan.
  • Mathieu R., Freeman C., Aryal J. (2007)—Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery. Landscape and Urban Planning, 81 (3): 179–192. doi: http://dx.doi.org/10.1016/j.landurbplan.2006.11.009.
  • Myint S.W., Lam N. (2005)—A study of lacunarity-based texture analysis approaches to improve urban image classification. Computers, environment and urban systems, 29 (5): 501–523. doi: http://dx.doi.org/10.1016/j.compenvurbsys.2005.01.007.
  • Myint S.W., Gober P., Brazel A., Grossman-Clarke S., Weng Q.H. (2011)—Per-pixel vs. Object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115 (5): 1145–1161. doi: http://dx.doi.org/10.1016/j.rse.2010.12.017.
  • Niemeyer J., Rottensteiner F., Soergel U. (2014)—Contextual classification of lidar data and building object detection in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 152–165. doi: http://dx.doi.org/10.1016/j.isprsjprs.2013.11.001.
  • Plotnick R.E., Gardner R.H., O'neill R.V. (1993)—Lacunarity indices as measures of landscape texture. Landscape ecology, 8 (3): 201–211. doi: http://dx.doi.org/10.1007/BF00125351.
  • Rottensteiner F., Sohn G., Jung J., Gerke M., Baillard C., Benitez S., Breitkopf U. (2012)—The isprs benchmark on urban object classification and 3d building reconstruction. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. I-3: 293–298.
  • Rottensteiner F., Sohn G., Gerke M., Wegner J.D. (2013)—Isprs test project on urban classification and 3d building reconstruction. Commission III-Photogrammetric Computer Vision and Image Analysis, Working Group III/4-3D Scene Analysis, pp.1–17.
  • Smits P.C., Dellepiane S.G., Schowengerdt R.A. (1999)—Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost- based approach. International Journal of Remote Sensing, 20 (8): 1461–1486. doi: http://dx.doi.org/10.1080/014311699212560.
  • Thapa R.B., Murayama Y. (2009)—Urban mapping, accuracy & image classification: A comparison of multiple approaches in Tsukuba city, Japan. Applied geography, 29 (1): 135–144. doi: http://dx.doi.org/10.1016/j.apgeog.2008.08.001.
  • Uzar M. (2014)—Automatic building extraction with multi-sensor data using rule-based classification. European Journal of Remote Sensing, 47: 1–18. doi: http://dx.doi.org/10.5721/EuJRS20144701.