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Volumn , Issue , 2004, Pages 308-314

Discriminating urban environments using multi-scale texture and multiple SAR images

Author keywords

[No Author keywords available]

Indexed keywords

ORBITS; REMOTE SENSING; SPACE-BASED RADAR; SYNTHETIC APERTURE RADAR; URBAN GROWTH; URBAN PLANNING;

EID: 84945254315     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WARSD.2003.1295209     Document Type: Conference Paper
Times cited : (8)

References (18)
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  • 3
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    • Evaluation of the merging of SPOT multispectral and panchromatic data for classification of an urban environment
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    • Shaban, M.A.1    Dikshit, O.2
  • 4
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    • Estimation of urban vegetation abundance by spectral mixture analysis
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    • (2001) Int. J. Remote Sensing , vol.22 , Issue.7 , pp. 1305-1334
    • Small, C.1
  • 7
    • 0034271543 scopus 로고    scopus 로고
    • Nonlinear subsidence rate estimation using permanent scatterers in differential sar interferometry
    • Sept.
    • A. Ferretti, C. Prati, and F. Rocca, "Nonlinear Subsidence Rate Estimation Using Permanent Scatterers in Differential SAR Interferometry," IEEE Trans. on Geoscience and Remote Sensing, Vol. 38, N. 5, pp. 2002-2212, Sept. 2000.
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  • 8
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    • Texture-based characterization of urban environments on satellite SAR images
    • Jan.
    • F. Dell'Acqua, P. Gamba: "Texture-based characterization of urban environments on satellite SAR images," IEEE Trans. on Geoscience and Remote Sensing, Vol. 41, n. 11, pp. 153-159, Jan. 2003.
    • (2003) IEEE Trans. on Geoscience and Remote Sensing , vol.41 , Issue.11 , pp. 153-159
    • Dell'Acqua, F.1    Gamba, P.2
  • 10
    • 0031126891 scopus 로고    scopus 로고
    • Radar backscatter analysis for urban environments
    • Y. Dong, B. Forster and C. Ticehurst, "Radar backscatter analysis for urban environments," Int. J. Remote Sensing, vol. 18, no. 6, pp. 1351-1364, 1997.
    • (1997) Int. J. Remote Sensing , vol.18 , Issue.6 , pp. 1351-1364
    • Dong, Y.1    Forster, B.2    Ticehurst, C.3
  • 14
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    • ART neural networks for remote sensing: Vegetation classification from Landsat TM and terrain data
    • G.A. Carpenter, M.N. Gjaja, S. Gopal, and C.E. Woodcock, "ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain data", IEEE Trans. Geoscience and Remote Sensing, Vol. 35, pp. 308-325, 1997.
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  • 15
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    • Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images
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    • M. Pesaresi, J.A. Benediktsson, "A New Approach for the Morphological Segmentation of High-Resolution Satellite Imagery," IEEE Trans. on Geoscience and Remote Sensing, Vol. 39, n. 2, pp. 309-320, Feb. 2001.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.