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Volumn 26, Issue 22, 2005, Pages 4981-4997

A competitive pixel-object approach for land cover classification

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; IMAGE SEGMENTATION; LAND USE; NEURAL NETWORKS; PATTERN RECOGNITION;

EID: 33745081013     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160500213912     Document Type: Review
Times cited : (54)

References (33)
  • 2
    • 0001812168 scopus 로고    scopus 로고
    • Multi-resolutional segmentation: An optimization approach for high quality multi-scale image segmentation
    • Baatz, M. and Schape, A.(2000) Multi-resolutional segmentation: An optimization approach for high quality multi-scale image segmentation. In Angewandte Geographische Informationsverarbeitung XII. (pp. 12-23).
    • (2000) Angewandte Geographische Informationsverarbeitung XII , pp. 12-23
    • Baatz, M.1    Schape, A.2
  • 3
    • 0035382417 scopus 로고    scopus 로고
    • Per-parcel land use classification in urban areas applying a rule-based technique
    • Bauer, T. and Steinnocher, K. (2001) Per-parcel land use classification in urban areas applying a rule-based technique. GeoBIT/GIS, 6, pp. 12-17.
    • (2001) GeoBIT/GIS , vol.6 , pp. 12-17
    • Bauer, T.1    Steinnocher, K.2
  • 7
    • 0027525734 scopus 로고
    • Artificial neural networks for land cover classification and mapping
    • Civco, D. L. (1993) Artificial neural networks for land cover classification and mapping. International Journal of Geographic Information Systems, 7, pp. 173-186.
    • (1993) International Journal of Geographic Information Systems , vol.7 , pp. 173-186
    • Civco, D.L.1
  • 8
    • 0001174666 scopus 로고    scopus 로고
    • Impervious surface mapping for the state of Connecticut
    • Seattle, WA
    • Civco, D. L. and Hurd, J. D.(1997) Impervious surface mapping for the state of Connecticut. In Proceedings of ASPRS/ACSM Annual Convention. (pp. 124-135). Seattle, WA
    • (1997) Proceedings of ASPRS/ACSM Annual Convention , pp. 124-135
    • Civco, D.L.1    Hurd, J.D.2
  • 11
    • 0024861871 scopus 로고
    • Approximation by superpositions of a sigmoidal function
    • Cybenko, G. (1989) Approximation by superpositions of a sigmoidal function. Mathematical Control Signals Systems, 2, pp. 303-314.
    • (1989) Mathematical Control Signals Systems , vol.2 , pp. 303-314
    • Cybenko, G.1
  • 12
    • 33745090412 scopus 로고    scopus 로고
    • DEFINIENS
    • DEFINIENS. 2001. http://www.definiens.com
    • (2001)
  • 18
    • 33644949658 scopus 로고    scopus 로고
    • Detecting information settlements from IKONOS image data using methods of object oriented image analysis: An example from Cape Town (South Africa)
    • Regensburg Germany 22-23, June
    • Hofmann, P.(2001). Detecting information settlements from IKONOS image data using methods of object oriented image analysis: An example from Cape Town (South Africa). 2nd Symposium on Remote Sensing of Urban Areas Regensburg Germany 22-23, June. In [pp. 41-42].
    • (2001) 2nd Symposium on Remote Sensing of Urban Areas , pp. 41-42
    • Hofmann, P.1
  • 19
    • 0011930855 scopus 로고
    • Neural network image classification
    • Cleveland
    • Howald, K. J.(1989) Neural network image classification. In Proceedings of ASPRS-ACSM. (pp. 207-215). Cleveland
    • (1989) Proceedings of ASPRS-ACSM , pp. 207-215
    • Howald, K.J.1
  • 21
    • 0002704818 scopus 로고
    • A practical Bayesian framework for back-propagation networks
    • MacKay, D. J. C. (1992a) A practical Bayesian framework for back-propagation networks. Neural Computation, 4, pp. 448-472.
    • (1992) Neural Computation , vol.4 , pp. 448-472
    • MacKay, D.J.C.1
  • 22
    • 0000234257 scopus 로고
    • The evidence framework applied to classification networks
    • MacKay, D. J. C. (1992b) The evidence framework applied to classification networks. Neural Computation, 4, pp. 720-736.
    • (1992) Neural Computation , vol.4 , pp. 720-736
    • MacKay, D.J.C.1
  • 23
    • 0002595536 scopus 로고
    • Generalization and parameter estimation in feedforward nets
    • California: Morgan Kaufman
    • Morgan, N. and Bourlard, H.(1990) Generalization and parameter estimation in feedforward nets. In Advances in Neural Information Processing Systems II. (pp. 598-605). California: Morgan Kaufman.
    • (1990) Advances in Neural Information Processing Systems , vol.2 , pp. 598-605
    • Morgan, N.1    Bourlard, H.2
  • 24
  • 25
    • 0027658896 scopus 로고
    • A review on image segmentation techniques
    • Pal, N. R. and Pal, S. K. (1993) A review on image segmentation techniques. Pattern Recognition, 26, pp. 1277-1294.
    • (1993) Pattern Recognition , vol.26 , pp. 1277-1294
    • Pal, N.R.1    Pal, S.K.2
  • 26
    • 0029415649 scopus 로고
    • A review and analysis of backpropagation neural network for classification of remotely-sensed multi-spectral imagery
    • Paola, J. D. and Schowengerdt, R. A. (1995) A review and analysis of backpropagation neural network for classification of remotely-sensed multi-spectral imagery. International Journal of Remote Sensing, 16, pp. 3033-3058.
    • (1995) International Journal of Remote Sensing , vol.16 , pp. 3033-3058
    • Paola, J.D.1    Schowengerdt, R.A.2
  • 27
    • 0001595997 scopus 로고
    • Neural network classifiers estimate Bayesian a posteriori probabilities
    • Richard, M. D. and Lippmann, R. P. (1991) Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Computation, 3, pp. 461-483.
    • (1991) Neural Computation , vol.3 , pp. 461-483
    • Richard, M.D.1    Lippmann, R.P.2
  • 29
    • 0000646059 scopus 로고
    • Learning internal reprsentations by error propagation
    • Cambridge MA: MIT Press
    • Rumelhart, D. E. and Hinton, G. E. and Williams, R. J.(1986) Learning internal reprsentations by error propagation. In Parallel Distributed Processing. (pp. 318-362). Cambridge MA: MIT Press.
    • (1986) Parallel Distributed Processing , pp. 318-362
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 30
    • 33745078835 scopus 로고    scopus 로고
    • Hybrid pixel-object pattern recognition in remote sensing
    • PhD dissertation, University of Connecticut
    • Song, M.(2002) Hybrid pixel-object pattern recognition in remote sensing. In. PhD dissertation, University of Connecticut
    • (2002)
    • Song, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.