19. Ehlers, M., Multisensor image fusion techniques in remote
sensing. ISPRS Journal of Photogrammetric Engineering and
Remote Sensing, vol. 46, 1991.
20. Förstner, W., Quality assessment of object location and point-
transfer using digital image correlation techniques. International
Archives of Photogrammetry and Remote Sensing, Rio de
Janeiro, Comm. Ill, vol. 25, part A3a, p. 197-219.
21. Förstner, W., Object extraction from digital images. Schriften-
reihe des Institut fiir Photogrammetrie der Universitat Stuttgart,
Heft 15, p. 269-279.
22. Förstner, W., Least squares matching. Haralick/Shapiro (Edi
tors) Robot and Computer Vision, vol. 2, Addison Wesley, 1992.
23. Förstner, W., Feature extraction for digital photogrammetry.
Photogrammetric Record, vol. 14, no. 82, October 1993.
24. Förstner, W., St. Ruwiedel, Robust computer vision. Wichmann,
Karlsruhe, 1992.
25. Fu, K. S., Syntactic pattern recognition and applications. Pren
tice Hall, Englewood Cliffs, NJ, 1982.
26. Fua, P., A. J. Hanson, Objective functions for feature discrimi
nation: theory. Proceedings DARPA Image Understanding
Workshop, 1989.
27. Geman, S., D. Geman, Stochastic relaxation, Gibbs distribution
and the bayesian restoration of images. IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 6, p. 721 - 741.
28. Geoffrion, A. M., The SML language for structured modelling:
levels 1 and 2. (to appear in Operations Research).
29. Geoffrion, A. M., The SML language for structured modelling
levels 3 and 4. (to appear in Operations Research).
30. Günther, O., H. J. Schek, (Editors), Advances in spatial data
bases. Lecture Notes in Computer Science 525, Berlin, 1991.
31Guindon, B., Multi-temporal scene analysis: a tool to aid in the
identification of cartographically significant edge features on
satellite imagery. Canadian Journal of Remote Sensing, vol. 14,
no. 1, May 1988, p. 38-45.
32. Hanson, A. J., L. H. Quam, Overview of the SRI cartographic
modelling environment. Proceedings Image Understanding
Workshop, Cambridge, Mass., 1988, p. 576-582.
33. Hayes, P. J., Some problems and non-problems in represen
tation theory. Proceedings AISB, Summer Conference, Univer
sity of Sussex, 1974, p. 63 - 79.
34. Hayes, P. J., The second naive physics manifesto. Formal
Theories of the Common. (J. R. Hobbs and R. C. Moore,
Editors).
35. He, G., J. Jansa, Fine radiometrische Anpassungsmethode für
die Mosaikherstellung aus digitalen Bildern. Zeitschrift für
Photogrammetrie und Fernerkundung, 1990, vol. 58, no. 2,
p. 43 - 49.
36. Heipke, Ch. A global approach for least squares image
matching and surface reconstruction in object space. Photo
grammetric Engineering and Remote Sensing, vol. 58, no. 3,
p. 317-323.
37. Helava, U. V. Object-space least-squares correlation. Photo
grammetric Engineering and Remote Sensing, vol. 54, no. 6,
p. 711 - 714.
38. Herman, M., T. Kanade, The 3D-mosaic scene understanding
systemincremental reconstruction of 3D scenes from complex
images. Readings in Computer Vision, (Fischler and Firschein,
Editors) Kaufmann, 1987, p. 471 -482.
39. Horn, B. K. P., M. Brooks, Shape from shading. The MIT-Press,
Cambridge, Massachusetts, 1990.
40. Hoyano, A., Y. Komatsu Y., Influence of mixels on land cover
classification in residential areas using airborne MSS data.
International Archives of Photogrammetry and Remote Sensing,
Kyoto 1988, p. VII - 399.
41. Hummel, R. A., S. W. Zucker, On the foundations of relaxation
labelling processes. IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 5., 1983, p. 267-286.
42. Hutchinson, C. F., Techniques for combining LANDSAT and
ancillary data for digital classification improvement. Photo
grammetric Engineering and Remote Sensing, vol. 48, no. 1,
p. 123- 130.
43. Janssen, L. L. F., M. N. Jaarsma, E. T. van der Linden, Inte
grating topographic data with remote sensing for land cover
classification. Photogrammetric Engineering and Remote Sen
sing, vol. 56, no. 11, November 1990, p. 1503- 1506.
44. Janssen, L. L. F., J. van Amsterdam, An object-based approach
to the classification of remotely sensed images. Proceedings
IGARSS-Symposium 1991, Espoo, Finland.
45. Jarczyk, G., Untersuchungen zur Texturanalyse. Diplomarbeit,
Institut für Photogrammetrie der Universitat Bonn.
46. Kent, W., The breakdown of the information model in multi-
database systems. Sheth 1991, p. 10-15.
47. Klir, G. J., T. A. Folger, Fuzzy sets, uncertainty and information.
Prentice Hall, 1988.
48. Konecny, G., Der Einsatz von Fernerkundungsdaten in GIS.
Fernerkundung in der Forstwirtschaft (Oesten, Kuntz and
Gross, Editors), Wichmann, Karlsruhe.
49. Laan, F. B. van der, Improvement of classification results from
a satellite image using context information from a topographic
map. International Archives of Photogrammetry and Remote
Sensing, Kyoto 1988, VII - 98.
50. Lawton, D. T., T. S. Levitt, C. McConell, J. Glicksmann, Terrain
models for an autonomous land vehicle. Readings in Computer
Vision, (Fischler and Firschein, Editors) Kaufmann, 1987, p.
483 - 491
51Leclerc, Y. G., Constructing simple stable description for image
partitioning. International Journal on Computer Vision, vol. 3,
p. 73- 102.
52. Leclerc, Y. G., Region grouping using the minimum-description-
length principle. Proceedings Image Understanding Workshop,
1990, p. 473-481.
53. Lenat, D. B., R. V. Guka, Building large knowledge-based
systems: representation and inference in the CYC-project.
Addison Wesley Publishing Corporation, Reading, 1989.
54. Löcherbach, Th., Reconstruction of land-use units for the in
tegration of GIS and remote sensing data. International Ar
chives of Photogrammetry and Remote Sensing, TC7 Workshop
on Multi-source Data Integration with Respect to Land Inventory
Applications, September, 1992, Delft.
55. Lowe, D.G., Perceptual organization and visual recognition.
Kluwer Academic Publisher.
56. Malik, J., P. Perona, Detecting and localizing edges composed
of steps, peaks and roofs. Proceedings 3rd International Con
ference on Computer Vision, Osaka, Japan, 1990, p. 52 - 57.
57. Malik, J., P. Perona, Pre-attentive texture discrimination with
early vision mechanisms. Journal of the Optical Society of
America.
58. March, S. E., P. Switzer, R. J. P. Kowalik, Resolving the per
centage of component terrains within single resolution ele
ments. Photogrammetric Engineering and Remote Sensing, vol.
46, no. 8, p. 1079 - 1086.
59. Merchant, J., Using spatial logic in classification of LANDSAT
TM data. Proceedings IEEE IX Pecora symposium on spatial
information technologies for Remote Sensing today and to
morrow, p. 378 - 385.
60. Middelkoop, H., L. L. F. Janssen, Implementation of temporal
relationships in knowledge-based classification of satellite ima
ges. Photogrammetric Engineering and Remote Sensing, vol.
57, no. 7, p. 937 - 945.
61. Minsky, M. L., A framework for representing knowledge. The
Psychology of Computer Vision, (P. H. Winston, Editor) 1975
New York, McGraw-Hill, 1975.
62. Modestino, J. W., J. Zhang, A Markov random field model-based
approach to image interpretation. IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 14., no. 6, 1992, p.
606-615.
63. Moller-Jensen, L., Knowledge-based classification of an urban
area using texture and context information in LANDSAT-TM
imagery. Photogrammetric Engineering and Remote Sensing,
vol. 56, no. 6, p. 899 - 904.
64. Mohan, R., R. Nevatia, Perceptual grouping for the detection
and description of structures in aerial images. IRIS, University
of South California, Report no. 225, Los Angeles, 1987.
65. Molenaar, M., Formal data structures, object dynamics and
consistency rules. Digital Photogrammetric Systems (Ebner/
Heipke/Fritsch, Editors) Wichmann, Karlsruhe, 1991.
66. Molenaar, M., D. Fritsch, Combined data structures for vector
and raster representation in Geographic Information Systems.
GIS, vol. 4, no. 5, Karlsruhe, 1991, p. 26-32.
67. Niemann, H., G. F. Sagerer, St. Schroder, F. Kummert, ER
NEST: A semantic network system for pattern analysis. IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol.
12, no. 9, 1990, p. 883-905.
68. Nilsson, N. J., Principles of artificial intelligence. Palo Alto,
California, Tioga, 1980.
69. Palubinskas, G., A comparative study of decision making in
images modeled by Gaussian-Markov random fields. Interna
tional Journal of Pattern Recognition and Artificial Intelligence,
vol. 2 no. 4, p. 621 - 639.
70. Pan, H.-P., A spatial structure theory in machine vision and its
application to structural and textural analysis of remotely sen
sed images. Ph.D.-thesis, University Twente, ITC, Enschede.
71. Pan, H.-P., W. Förstner, Stochastic polygon map grammers: a
generic model for understanding landuse maps and images in
remote sensing. (Förstner/Ruwiedel, Editors), 1992.
382
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