PE&RS August 2015 - page 677

Pu, R., S. Landry, and Q. Yu, 2011. Object-based urban detailed land
cover classification with high spatial resolution IKONOS imagery,
International Journal of Remote Sensing
, 32(12):3285–3308.
Radoux, J., P. Bogaert, and P. Defourny, 2010. Overall accuracy
estimation for geographic object-based image classification,
Accuracy 2010 Symposium
, Leicester, UK.
Richard, A.J., and X. Jia, 2006.
Remote Sensing Digital Image Analysis:
An Introduction to 4
th
Edition
, Springer, Heidelberg, 209 p.
Rittl, T., M. Cooper, R.J. Heck, and M.V.R. Ballester, 2013. Object-
based method outperforms per-pixel method for land cover
classification in a protected area of the Brazilian Atlantic
rainforest region,
Pedosphere
, 23(3):290–297: doi:http://dx.doi.
org/10.1016/S1002-0160(13)60018-1
Schöpfer, E., S. Lang, and F. Albrecht, 2008. Object-fate analysis:
Spatial relationships for the assessment of object transition and
correspondence,
Object-Based Image Analysis
(T.
Blaschke, S. Lang, and G.J. Hay, editors), Springer, Berlin, Heidelberg,
pp. 786–801.
Stolz, R., M. Braun, M. Probeck, R. Weidinger, and W. Mauser, 2005.
Land use classi cation in complex terrain: The role of ancillary
knowledge,
EARSeLeProceedings
, 4(1):94–105.
Story, M., and R.G. Congalton, 1986. Accuracy assessment: A user’s
perspective,
Photogrammetric Engineering & Remote Sensing,
52(3):397–399.
Tehrany, M.S., B. Pradhan , and M.N. Jebu, 2013. A comparative
assessment between object and pixel-based classification
approaches for land use/land cover mapping using SPOT 5
imagery,
Geocarto International
, pp. 1–19: doi:10.1080/1010604
9.2013.768300
Tiede, D., S. Lang, and D. Hölbling, 2008. Class modelling of biotope
complexes - Success and remaining challenges,
Proceedings of
GEOBIA, 2008 - Pixels, Objects, Intelligence: GEOgraphic Object
Based Image Analysis for the 21
st
Century,
05-8 0August, Calgary,
Alberta, Canada, pp. 6768–6774.
Trimble, 2011, Reference manual InphoOrthoVista 4.6, pp. 56–65,
URL:
ftp://76.162.39.185/INPHO/ReferenceManual_OrthoVista_
(English)_46.pdf
(last date accessed: 03 June 2015).
Tso, B., and P.M. Mather, 2009.
Classification Methods for Remotely
Sensed Data
, Taylor and Francis, 95 p.
Xu, W., B. Wu, J. Huang, Y. Zhang, and Y. Tian, 2004. Segmentation
and classification approach of land cover mapping using
QuickBird image, IGARSS ‘04,
Proceedings 2004 IEEE Inter-
national (Volume 5)
, 2024, Anchorage, Alaska, pp. 3368–3370.
Yu, Q., P. Gong, N. Clinton, G. Biging, M. Kelly, and D. Schirokauer,
2006. Object-based detailed vegetation classification with
airborne high spatial resolution remote sensing imagery,
Photogrammetric Engineering & Remote Sensing
, 72(7):799–811.
(Received 12 December 2013; accepted 08 April 2014; final
version 25 September 2014)
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