135
Views
4
CrossRef citations to date
0
Altmetric
Articles

Automatic detection of urban area from the remote sensing imagery based on improved D-S evidence theory

&
Pages 261-269 | Received 21 Dec 2016, Accepted 02 May 2017, Published online: 16 May 2017

References

  • Gamba P, Dell’ Acqua F, Lisini G, et al. Improved VHR urban area mapping exploiting object boundaries. IEEE T Geosci Remote. 2007;45(8):2676–2682. doi: 10.1109/TGRS.2007.899811
  • Pesaresi M, Gerhardinger A, Kayitakire F. A robust built-up area presence index by anisotropic rotation-invariant textural measure. IEEE J Selected Topics Appl Earth Obs Remote Sens. 2008;1(3):180–192. doi: 10.1109/JSTARS.2008.2002869
  • Benediktsson JA, Pesaresi M, Arnason K. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE T Geosci Remote. 2003;41(9):1940–1949. doi: 10.1109/TGRS.2003.814625
  • Su W, Li J, Chen Y, et al. Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery. Int J Remote Sens. 2008;29(11):3105–3117. doi: 10.1080/01431160701469016
  • Zhong P, Wang R. A multiple conditional random fields ensemble model for urban area detection in remote sensing optical images. IEEE T Geosci Remote. 2007;45(12):3978–3988. doi: 10.1109/TGRS.2007.907109
  • Li Z, Ma J, Zhang R, et al. Urban areas detection based on conditional random field and multiscale textural features. Remote Sens GIS Data Process. 2009;7498:74981I-1–74981I-6.
  • Kaur I, Gill R. Super-pixel based segmentation of urban area and its detection using machine learning techniques. Bull Electr Eng Inform. 2016;5(4):100–109.
  • Salehi B, Zhang Y, Zhong M, et al. Object-based classification of urban areas using VHR imagery and height points ancillary data. Remote Sens. 2012;4(8):2256–2276. doi: 10.3390/rs4082256
  • Aguilar M A, Fernández A, Aguilar F J, et al. Classification of urban areas from GeoEye-1 imagery through texture features based on histograms of equivalent patterns. Eur J Remote Sens. 2016;49:93–120. doi: 10.5721/EuJRS20164906
  • Sirmacek B, Unsalan C. Urban-area and building detection using SIFT keypoints and graph theory. IEEE Trans Geosci Remote Sens. 2009;47(4):1156–1167. doi: 10.1109/TGRS.2008.2008440
  • Weizman L, Goldberger J. Detection of urban zones in satellite images using visual words. Geosci Remote Sens Symp. 2008;160–163.
  • Montoya-Zegarra JA, Wegner JD, Ladický L, et al. Semantic segmentation of aerial images in urban areas with class-specific higher-order cliques. ISPRS Ann Photogramm Remote Sens Spatial Inform Sci. 2015;II-3/W4:127–133. doi: 10.5194/isprsannals-II-3-W4-127-2015
  • Unsalan C, Boyer KL. Classifying land development in high-resolution panchromatic satellite images using straight-line statistics. IEEE Trans Geosci Remote Sens. 2004;42(4):907–919. doi: 10.1109/TGRS.2003.818835
  • Maktav D, Uysal C. Detection of urban expansion by using DMSP-OLS, landsat data and linear spectral unmixing method. Int J 3D Inform Model. 2015;4(2):58–67.
  • Ma T, Zhou Y, Zhou C, et al. Night-time light derived estimation of spatio-temporal characteristics of urbanization dynamics using DMSP/OLS satellite data. Remote Sens Environ. 2015;158(158):453–464. doi: 10.1016/j.rse.2014.11.022
  • Huang X, Schneider A, Friedl MA. Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights. Remote Sens Environ. 2016;175:92–108. doi: 10.1016/j.rse.2015.12.042
  • Tran G, Nguyen D, Milenkovic M, et al., Potential of full waveform airborne laser scanning data for urban area classification – transfer of classification approaches between missions. 36th International Symposium on Remote Sensing of Environment; 2015; Berlin, Germany. p. 1317–1323.
  • Rastiveis H. Decision level fusion of LIDAR data and aerial color imagery based on Bayesian theory for urban area classification. ISPRS Int Arch Photogram Remote Sens Spatial Inform Sci. 2015;XL-1-W5:589–594. doi: 10.5194/isprsarchives-XL-1-W5-589-2015
  • Hamedianfar A, Shafri H ZM, Mansor S, et al. Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data. Int J Remote Sens. 2014;35(5):1876–1899. doi: 10.1080/01431161.2013.879350
  • Niemeyer J, Rottensteiner F, Soergel U, et al. Hierarchical higher order Crf for the classification of airborne LIDAR point clouds in urban areas. ISPRS Int Arch Photogram Remote Sens Spatial Inform Sci. 2016;XLI-B3:655–662. doi: 10.5194/isprsarchives-XLI-B3-655-2016
  • Lak AM, Zoej MJV. A New method for road detection in urban areas using high-resolution satellite images and lidar data based on fuzzy nearest-neighbor classification and optimal features. Arab J Geosci. 2016;9(5):1–11. doi: 10.1007/s12517-016-2374-1
  • Azmedroub B, Ouarzeddine M, Souissi B. Extraction of urban areas from polarimetric SAR imagery. IEEE J Selected Topics Appl Earth Obs Remote Sens. 2016;9(6):2583–2591. doi: 10.1109/JSTARS.2016.2527242
  • Amitrano D, Belfiore V, Cecinati F, et al. Urban areas enhancement in multitemporal SAR RGB images using adaptive coherence window and texture information. IEEE J Selected Topics Appl Earth Obs Remote Sens. 2016;9(8):3740–3752. doi: 10.1109/JSTARS.2016.2555340
  • Xiang D, Ban Y, Su Y. Model-based decomposition with cross scattering for polarimetric SAR urban areas. IEEE Trans Geosci Remote Lett. 2015;12(12):1–5. doi: 10.1109/LGRS.2015.2499419
  • Benedek C, Descombes X, Zerubia J. Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics. IEEE Trans Pattern Anal. 2012;34(1):33–50. doi: 10.1109/TPAMI.2011.94
  • Ok AO. Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS J Photogramm Remote Sens. 2013;86(12):21–40. doi: 10.1016/j.isprsjprs.2013.09.004

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.