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Volumn Part F128815, Issue , 2013, Pages 1419-1426

Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery

Author keywords

Mil; Remote sensing; Spatial data mining

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; HOUSING; LEARNING SYSTEMS; MAPPING; PATTERN RECOGNITION; PIXELS; REMOTE SENSING; SATELLITE IMAGERY;

EID: 84973890288     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2487575.2488210     Document Type: Conference Paper
Times cited : (31)

References (26)
  • 2
    • 0025453627 scopus 로고
    • Neural network approaches versus statistical methods in classificaion of multisource remote sensing data
    • A. Benediktsson, P. Swain, and O. Ersoy. Neural network approaches versus statistical methods in classificaion of multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 28(4):550, 1990.
    • (1990) IEEE Transactions on Geoscience and Remote Sensing , vol.28 , Issue.4 , pp. 550
    • Benediktsson, A.1    Swain, P.2    Ersoy, O.3
  • 4
    • 80052338576 scopus 로고    scopus 로고
    • Application of multiple-instance learning for hyperspectral image analysis
    • IEEE, : sept
    • J. Bolton and P. Gader. Application of multiple-instance learning for hyperspectral image analysis. Geoscience and Remote Sensing Letters, IEEE, 8(5):889 -893, sept. 2011.
    • (2011) Geoscience and Remote Sensing Letters , vol.8 , Issue.5 , pp. 889-893
    • Bolton, J.1    Gader, P.2
  • 5
    • 0030618061 scopus 로고    scopus 로고
    • Multisource classification of complex rural areas by statistical and neural-network approaches
    • May
    • L. Bruzzone, C. Consese, F. Masellit, and F. Roli. Multisource classification of complex rural areas by statistical and neural-network approaches. Photogrammetric Engineering & Remote Sensing, 63(5):523-533, May 1997.
    • (1997) Photogrammetric Engineering & Remote Sensing , vol.63 , Issue.5 , pp. 523-533
    • Bruzzone, L.1    Consese, C.2    Masellit, F.3    Roli, F.4
  • 6
    • 79958264124 scopus 로고    scopus 로고
    • Scalable time series change detection for biomass monitoring using Gaussian process
    • V. Chandola and R. R. Vatsavai. Scalable time series change detection for biomass monitoring using gaussian process. In CIDU, pages 69-82, 2010.
    • (2010) CIDU , pp. 69-82
    • Chandola, V.1    Vatsavai, R.R.2
  • 7
    • 79960363460 scopus 로고    scopus 로고
    • A scalable Gaussian process analysis algorithm for biomass monitoring
    • V. Chandola and R. R. Vatsavai. A scalable gaussian process analysis algorithm for biomass monitoring. Statistical Analysis and Data Mining, 4(4):430-445, 2011.
    • (2011) Statistical Analysis and Data Mining , vol.4 , Issue.4 , pp. 430-445
    • Chandola, V.1    Vatsavai, R.R.2
  • 9
    • 0043126911 scopus 로고    scopus 로고
    • Logistic regression and artificial neural network classification models: A methodology review
    • S. Dreiseitl and L. Ohno-Machado. Logistic regression and artificial neural network classification models: A methodology review. Journal of Biomedical Informatics, 35(5-6):352 - 359, 2002.
    • (2002) Journal of Biomedical Informatics , vol.35 , Issue.5-6 , pp. 352-359
    • Dreiseitl, S.1    Ohno-Machado, L.2
  • 10
    • 84864741147 scopus 로고    scopus 로고
    • Image based characterization of formal and informal neighborhoods in an urban landscape. Selected topics in applied earth observations and remote sensing
    • aug
    • J. Graesser, A. Cheriyadat, R. Vatsavai, V. Chandola, J. Long, and E. Bright. Image based characterization of formal and informal neighborhoods in an urban landscape. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 5(4):1164 -1176, aug. 2012.
    • (2012) IEEE Journal of , vol.5 , Issue.4 , pp. 1164-1176
    • Graesser, J.1    Cheriyadat, A.2    Vatsavai, R.3    Chandola, V.4    Long, J.5    Bright, E.6
  • 11
    • 14644421528 scopus 로고    scopus 로고
    • Investigation of the random forest framework for classification of hyperspectral data
    • Mar
    • J. Ham, Y. Chen, M. M. Crawford, and J. Ghosh. Investigation of the random forest framework for classification of hyperspectral data. Geoscience and Remote Sensing, IEEE Transactions on, 43(3):492 - 501, Mar. 2005.
    • (2005) Geoscience and Remote Sensing, IEEE Transactions on , vol.43 , Issue.3 , pp. 492-501
    • Ham, J.1    Chen, Y.2    Crawford, M.M.3    Ghosh, J.4
  • 12
    • 0031106314 scopus 로고    scopus 로고
    • Strategies and best practice for neural network image classification
    • I. Kanellopoulos and G. G. Wilkinson. Strategies and best practice for neural network image classification. International Journal of Remote Sensing, 18(4):711-725, 1997.
    • (1997) International Journal of Remote Sensing , vol.18 , Issue.4 , pp. 711-725
    • Kanellopoulos, I.1    Wilkinson, G.G.2
  • 13
    • 21244437589 scopus 로고    scopus 로고
    • Sparse multinomial logistic regression: Fast algorithms and generalization bounds. Pattern analysis and machine intelligence
    • June
    • B. Krishnapuram, L. Carin, M. A. Figueiredo, and A. J. Hartemink. Sparse multinomial logistic regression: fast algorithms and generalization bounds. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(6):957 -968, June 2005.
    • (2005) IEEE Transactions on , vol.27 , Issue.6 , pp. 957-968
    • Krishnapuram, B.1    Carin, L.2    Figueiredo, M.A.3    Hartemink, A.J.4
  • 16
    • 0029341018 scopus 로고
    • A detailed comparison of backpropagation neural network and maximum likelihood classifiers for urban land use classification
    • J. Paola and R. Schowengerdt. A detailed comparison of backpropagation neural network and maximum likelihood classifiers for urban land use classification. IEEE Transactions on Geoscience and Remote Sensing, 33(4):981-996, 1995.
    • (1995) IEEE Transactions on Geoscience and Remote Sensing , vol.33 , Issue.4 , pp. 981-996
    • Paola, J.1    Schowengerdt, R.2
  • 17
    • 0030618062 scopus 로고    scopus 로고
    • The effect of neural-network structure on a multispectral land-use/land-cover classification
    • J. Paola and R. Schowengerdt. The effect of neural-network structure on a multispectral land-use/land-cover classification. Photogrammetric Engineering & Remote Sensing, 63(5):535-544, 1997.
    • (1997) Photogrammetric Engineering & Remote Sensing , vol.63 , Issue.5 , pp. 535-544
    • Paola, J.1    Schowengerdt, R.2
  • 18
    • 0036613147 scopus 로고    scopus 로고
    • Spatial contextual classification and prediction models for mining geospatial data
    • S. Shekhar, P. Schrater, R. Vatsavai, W. Wu, and S. Chawla. Spatial contextual classification and prediction models for mining geospatial data. IEEE Transaction on Multimedia, 4(2):174-188, 2002.
    • (2002) IEEE Transaction on Multimedia , vol.4 , Issue.2 , pp. 174-188
    • Shekhar, S.1    Schrater, P.2    Vatsavai, R.3    Wu, W.4    Chawla, S.5
  • 20
    • 72049131318 scopus 로고    scopus 로고
    • Multiple instance and context dependent learning in hyperspectral data. In hyperspectral image and signal Processing: Evolution in remote sensing, 2009
    • aug
    • P. Torrione, C. Ratto, and L. Collins. Multiple instance and context dependent learning in hyperspectral data. In Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS '09. First Workshop on, pages 1 -4, aug. 2009.
    • (2009) WHISPERS '09. First Workshop, on , pp. 1-4
    • Torrione, P.1    Ratto, C.2    Collins, L.3
  • 21
    • 84857153519 scopus 로고    scopus 로고
    • High-resolution urban image classification using extended features
    • R. R. Vatsavai. High-resolution urban image classification using extended features. In ICDM Workshops, pages 869-876, 2011.
    • (2011) ICDM Workshops , pp. 869-876
    • Vatsavai, R.R.1
  • 22
    • 84873125440 scopus 로고    scopus 로고
    • A data mining framework for monitoring nuclear facilities
    • Industry/Government Track
    • R. R. Vatsavai. A data mining framework for monitoring nuclear facilities. In ICDM Workshops (Industry/Government Track), page 917, 2012.
    • (2012) ICDM Workshops , pp. 917
    • Vatsavai, R.R.1
  • 25
    • 79960089324 scopus 로고    scopus 로고
    • Machine learning approaches for high-resolution urban land cover classification: A comparative study
    • R. R. Vatsavai, E. A. Bright, V. Chandola, B. L. Bhaduri, A. Cheriyadat, and J. Graesser. Machine learning approaches for high-resolution urban land cover classification: A comparative study. In COM.Geo, page 11, 2011.
    • (2011) COM.Geo , pp. 11
    • Vatsavai, R.R.1    Bright, E.A.2    Chandola, V.3    Bhaduri, B.L.4    Cheriyadat, A.5    Graesser, J.6
  • 26
    • 0141596676 scopus 로고    scopus 로고
    • Solving the multiple-instance problem: A lazy learning approach
    • Morgan Kaufmann
    • J. Wang. Solving the multiple-instance problem: A lazy learning approach. In Proc. 17th International Conf. on Machine Learning, pages 1119-1125. Morgan Kaufmann, 2000.
    • (2000) Proc. 17th International Conf. on Machine Learning , pp. 1119-1125
    • Wang, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.