International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004
Following the presentation of the various tools and their
contribution to the various phases and stages of the main
mapping operations of feature identification, feature extraction
and change detection, two case studies were presented, where
several image processing and spatial analysis tools and
techniques have been utilized. These examples demonstrated
the potential, applicability, usefulness and viability of this
concept towards the implementation of semi-automated
approaches for rapid and improved mapping operations and
results.
6. ACKNOWLEDGEMENTS
We wish to thank our team members Charles Baker, Pierre-
Alain Beauchesne, Johann Leveque, Anthony Pouw, Gord
Robertson and Ray Samson for their contributions to the
implementation of the two case studies.
7. REFERENCES
Armenakis C., F. Leduc, I. Cyr, F. Savopol, F. Cavayas, 2003.
A comparative analysis of scanned maps and imagery for
mapping applications. Inter. Journal of Photogrammetry &
Remote Sensing, Vol. 57, No. 5-6, pp. 304-314.
Auclair Fortier M.-F, D. Ziou, C. Armenakis, S. Wang, 2001.
Automated correction and updating of road databases from high
resolution imagery. Canadian Journal of Remote Sensing, Vol.
27, No. 1, pp. 76-89.
Baltsavias E.P., 2004. Object extraction and revision by image
analysis using existing geodata and knowledge: current status
and steps towards operational systems. Inter. Journal of
Photogrammetry & Remote Sensing, Vol. 58, No. 3-4, pp. 129-
151.
Canny J., 1986. A computational approach to edge detection.
IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 8, No. 6, pp. 670-698.
Cavayas F., Gonthier, E., Leduc, F., 2001. Planimetric
Features Enhancement of Landsat 7 Satellite Imagery using
Fusion Techniques. Contract Final Report submitted to Centre
for Topographic Information, Geomatics Canada, 77 p.
eCognition, 2003. User Guide 3. Definiens Imaging.
Eiumnoh A., R.P. Shrestha, 1997. Can DEM enhance the
digiatal image classification?
http://www. gisdevelopment.net/aars/acrs/| 997/ts9/ts9007pf.htm
(accessed 9 May 2004).
El-Hakim S., 1996. Vision-based automated measurement
techniques. In Close Range Photogrammetry and Machine
Vision, ed K.B Atkinson, Whittles Publishing, UK.
Forsythe K.W., 2004. Pansharpened Landsat 7 imagery for
improved urban area classification. GEOMATICA, Vol. 58, No.
1, pp. 23-31.
Haralick R.M., 1979. Statistical and structural approaches to
texture. Proceedings. IEEE, 67 (5), 786-803.
Hay G.J., T. Blaschke, D.J. Marceau, A. Bouchard, 2003. A
comparison of three image-object methods for multiscale
analysis of landscape structure. Inter. Journal of
Photogrammetry & Remote Sensing, Vol. 57, No. 5-6, pp. 327-
345.
Heipke C., D.-M. Straub, 1999. Towards the automatic GIS
update of vegetation areas from satellite imagery using digital
landscape model and prior information. /nternational Archives
of Photogrammetry and Remote Sensing, Vol. 32, Part 2W5,
pp. 167-174.
Hodgson M.E., J.R. Jensen, J.A. Tullis, K. Riordan, C.M.
Archer, 2003. Synergistic use of Lidar and Colour aerial
photography for mapping urban parcel imperviousness.
Photogrammetric Engineering & Remote Sensing, Vol. 69, No.
9, pp. 973-980.
Jodouin S., L Bentabet, D. Ziou, J. Vaillancourt, C. Armenakis,
2003. Spatial database updating using active contours for
multispectral images: application with Landsat 7. /nter. Journal
of Photogrammetry & Remote Sensing, Vol. 57, No. 5-6, pp.
346-355.
Kachouie N.N., J. Li, J. Alirezaie, 2004. A hybrid texture
segmentation method for mapping urban land use.
GEOMATICA, Vol. 58, No. 1, pp. 9-21.
Mather, P. M., 1987. Computer Processing of Remotely-Sensed
Images. Biddles Ltd., Guildford, Surrey, UK.
Pohl C., Touron, H., 2000. Issues and challenges of operational
applications using multisensor image fusion. Proc. Fusion of
Earth Data, T. Ranchin, L. Wald (Eds.), Sophia Antipolis,
France, pp. 25-31.
Schowengerdt R.A., 1997. Remote Sensing Models and
Methods for Image Processing. Academic Press, USA.
Singh A., 1989. Digital change detection techniques using
remotely-sensed data. /nter. Journal of Remote Sensing, Vol.
10, No. 6, pp. 989-1003.
Walter V., 2004. Object-based classification of remote sensing
data for change detection. Inter. Journal of Photogrammetry &
Remote Sensing, Vol. 58, No. 3-4, pp. 225-238.
Wang Z., D. Ziou, C. Armenakis, 2003. Combination of
imagery: A study of various methods, /GARSS 2003 CD-ROM
proceedings.
Willhauck G., 2000. Comparison of object oriented
classification techniques and standard image analysis for the use
of change detection between SPOT multi-spectral satellite
images and aerial photos. International Archives of
Photogrammetry and Remote Sensing, Vol. XXXIII, Part B3,
Amsterdam, pp. 214-211.
Zhang C., 2004. Towards an operational system for automated
updating of road database by integration of imagery and
geodata. . Inter. Journal of Photogrammetry & Remote Sensing,
Vol. 58, No. 3-4, pp. 166-186.
Zhang, Y., 2001. Texture integrated classification of urban trecd
areas in high-resolution colour infrared ^ imagery.
Photogrammetric Engineering & Remote Sensing, Vol. 67, No.
12, pp. 1359-1365.
616
(IN
ABS]
This |
autom
point
proce
algori
simul.
geneti
expan
Label
label
releva
label I
map le
compu
autom
been
autom:
This 1
optima
algorit
search
biolog;
includi
learnin
simple
experit
for aut
II.
It has ,
feature
methoc
Imhof
Zoraste
20% ce
develo|
The di:
develoy
searchi
and Ge
algoritl
