Baatz, M., and A. Shäpe, 2000. Multiresolution segmentation:
an optimization approach for high quality multi-
scale image segmentation,
Angewandte Geographische
Informationsverarbeitung XI
(J. Strobl, T. Blaschke, and G.
Griesebner, editors)
.
Salzburg: Herbert Wichmann Verlag,
Heidelberg, pp. 12–23.
Benarchid, O., and N. Raissouni, 2013. Support vector machines for
object based building extraction in suburban area using very
high resolution satellite images, A case study: Tetuan, Morocco,
International Journal of Artificial Intelligence
, 2(1):43–50.
Benz, U., M. Baatz, and G. Schreier, 2001. Oscar - object oriented
segmentation and classification of advanced radar allow
automated information extraction,
Proceedings of the
International Geoscience and Remote Sensing Symposium
(IGARSS)
, 4:1913–1915.
Benz, U.C., P. Hofmann, G. Willhauck, I. Lingenfelder, and M.
Heynen, 2004. Multiresolution,object-oriented fuzzy analysis of
remote sensing data for GIS-ready information,
ISPRS Journal of
Photogrammetry and Remote Sensing
, 58(3-4):239–258.
Blaschke, T., 2010. Object-based image analysis for remote sensing,
ISPRS Journal of Photogrammetry and Remote Sensing
, 65(1): 2–16.
Blaschke, T., G.J. Hay, M. Kelley, S. Lang, P. Hofmann, E. Addink,
R.-Q. Feitosa, F. nan der Meer, H. van der Werff, F. van Coillie,
and D. Tiede, 2014. Geographic Object-Based Image Analysis -
Towards a new paradigm,
ISPRS Journal of Photogrammetry and
Remote Sensing
, 87(2014):180–191.
Burns, J.B., A.R. Hanson, and E.M. Riseman, 1986. Extracting straight
lines, IEEE
Transactions on
Pattern
Analysis and Machine
Intelligence
, 8(4):425–456.
Canny, J.F., 1986. A computational approach to edge detection, IEEE
Transactions on
Pattern
Analysis and Machine Intelligence
,
PAMI-8(6):679–698.
Chen, J., J. Li, D. Pan, Q. Zhu, and Z. Mao, 2012. Edge-guided
multiscale segmentation of satellite multispectral imagery,
IEEE
Transactions on Geoscience Remote Sensing
, 50(11):4513–4520.
d’Oleire-Oltmanns, S., I. Marzolff, D. Tiede, and T. Blaschke, 2014.
Detection of gully-affected areas by applying Object-Based Image
Analysis (OBIA) in the Region of Taroudannt, Morocco,
Remote
Sensing
, 6(9):8287–8309.
Dr
ǎ
gu
ţ
, L., D. Tiede, and S.R. Levick, 2010. ESP: A tool to estimate
scale parameter for multiresolution image segmentation of
remotely sensed data,
International Journal of
Geographical Information Science
, 24(6):859–871.
Dr
ǎ
gu
ţ
, L., O. Csillik, C. Eisank, and D. Tiede, 2014. Automated
parameterization for multi-scale image segmentation on multiple
layers,
ISPRS Journal for Photogrammetry and Remote Sensing
,
88:119–127.
Du, Y., D. Wu, F. Liang, and C. Li, 2013. Integration of case-based
reasoning and object-based image classification to classify SPOT
images: A case study of aquaculture land use mapping in coastal
areas of Guangdong Province, China,
GIScience & Remote
Sensing
, 50(5):574-589.
Gao, Y., P. Marpu, I. Niemeyer, and D. Runfola, 2011. Object-based class-
ification with features extracted by a semi-automatic feature ex-
traction algorithm - SEaTH,
Geocarto International
, 26(3):211–226.
Hebel, M., M. Arens, and U. Stilla, 2013. Change detection in urban
areas by object-based analysis and on-the-fly comparison of
multi-view ALS data,
ISPRS Journal on Photogrammetry and
Remote Sensing
, 86:52–64.
Hu, X., and Q. Weng, 2011. Impervious surface area extraction from
IKONOS imagery using an object-based fuzzy method,
Geocarto
International
, 26(1):3–20.
Huang, X., and L. Zhang, 2008. An adaptive mean-shift analysis
approach for object extraction and classification from urban
hyperspectral imagery,
IEEE Transactions on Geoscience and
Remote Sensing
, 46(12):4173–4184.
Johansen, K., L.A. Arroyo, S. Phinn, and C. Witte, 2010. Comparison
of geo-object based and pixel-based change detection of riparian
environments using high spatial resolution multi-spectral
imagery,
Photogrammetric Engineering & Remote Sensing
,
76(2):123–136.
Kermad, C.D., and K. Chehdi, 2002. Automatic image segmentation
system through iterative edge-region co-operation,
Image and
Vision Computing
, 20(8):541–555.
Laliberte, A.S., D.M. Browning, and A. Rango, 2012. A comparison
of three feature selection methods for object-based classification
of sub-decimeter resolution UltraCam-L imagery,
International
Journal of Applied Earth Observation and Geoinformation
,
15:70–78.
Li, D., G. Zhang, Z. Wu, and L. Yi, 2010. An edge embedded marker-
based watershed algorithm for high spatial resolution remote
sensing image segmentation,
IEEE Transactions on Image
Processing
, 19(10):2781–2787.
Lin, C.H., and C.C. Chen, 2010. Image segmentation based on edge
detection and region growing for thinprep-cervical smear,
International Journal of Pattern Recognition
, 24(7):1061–1089.
Lu, P., A. Stumpf, and N. Kerle, 2011. Object-oriented change
detection for landslide rapid mapping,
IEEE Geoscience and
Remote Sensing Letters
, 8(4):701–705.
Meinel, G., and M. Neubert, 2004. A comparison of segmentation
programs for high resolution remote sensing data,
International
Archives of Photogrammetry
, 35:1097–1105.
Rasi, R., R. Beuchle, C. Bodart, and M. Vollmar, 2013. Automatic
updating of an object-based tropical forest cover classification
and change assessment,
IEEE Journal of Selected Topics in
Applied Earth Observations and Remote Sensing
, 6(1):66–73.
Salehi, B., Y. Zhang, and M. Zhong, 2012. Object-based classification
of urban areas using VHR imagery and height points ancillary
data,
Remote Sensing
, 4(8):2256–2276.
Sebari, I., and D.C. He, 2013. Automatic fuzzy object-based analysis
of VHSR images for urban objects extraction,
ISPRS Journal
Photogrammetry Remote Sensing
, 79:171–184.
Smith, G.M., and R.D. Morton, 2010. Real world objects in Geographic
Object-Based Image Analysis through the exploitation of existing
digital cartography and image segmentation,
Photogrammetric
Engineering & Remote Sensing
, 76(2):163–171.
Tiede, D., S. Lang, F. Albrecht, and D. Holbling, 2010. Object-based
class modeling for cadastre constrained delineation of geo-
objects,
Photogrammetric Engineering & Remote Sensing
,
76(2):193–202.
Tiede, D., S. Lang, P. Fureder, D. Holbling, C. Hoffmann, and P.
Zeil, 2011. Automated damage indication for rapid geospatial
reporting,
Photogrammetric Engineering & Remote Sensing
,
77(9): 933–942.
Tong, H., T. Maxwell, Y. Zhang, and V. Dey, 2012. A supervised and
fuzzy-based approach to determine optimal multi-resolution
image segmentation parameters,
Photogrammetric Engineering &
Remote Sensing
, 78(10):1029–1044.
Tullis, J.A., J.R. Jensen, G.T. Raber, and A.M. Filippi, 2010. Spatial
scale management experiments using optical aerial imagery and
LIDAR data synergy,
GIScience & Remote Sensing
, 47(3):338–359.
Vincent, L., and P. Soille, 1991. Watershed in digital spaces: an
efficient algorithm based on immersion simulations, IEEE
Transactions on
Pattern
Analysis and Machine Intelligence
,
13(6):583–598.
Walter, V., 2004. Object-based classification of remote sensing data
for change detection,
ISPRS Journal Photogrammetry Remote
Sensing
, 58(3):225–238.
Wang, M., and R. Li, 2014. Segmentation of high spatial resolution
remote sensing imagery based on hard-boundary constraint and
two-stage merging,
IEEE Transactions on Geoscience and Remote
Sensing
, 52(9):5712–5725.
Wang, M., Y. Sun, and G. Chen, 2015. Refining high spatial resolution
remote sensing image segmentation for man-made objects
through a collinear and ipsilateral neighborhood model,
Photogrammetric engineering & Remote sensing
, 81(5):397–406.
Yu, P., A.K. Qin, and D.A. Clausi, 2012. Unsupervised polarimetric
SAR image segmentation and classification using region growing
with edge penalty,
IEEE Transactions on Geoscience and Remote
Sensing
, 50(4):1302–1317.
(Received 18 June 2015; accepted 31 August 2015; final ver-
sion 07 September 2015)
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