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      • Faculty of Engineering
      • Department of Computer Engineering
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      Interactive training of advanced classifiers for mining remote sensing image archives

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      Author
      Aksoy, Selim
      Koperski, K.
      Tusk, C.
      Marchisio G.
      Date
      2004
      Source Title
      KDD '04 Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
      Publisher
      ACM
      Pages
      773 - 782
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Advances in satellite technology and availability of down-loaded images constantly increase the sizes of remote sensing image archives. Automatic content extraction, classification and content-based retrieval have become highly desired goals for the development of intelligent remote sensing databases. The common approach for mining these databases uses rules created by analysts. However, incorporating GIS information and human expert knowledge with digital image processing improves remote sensing image analysis. We developed a system that uses decision tree classifiers for interactive learning of land cover models and mining of image archives. Decision trees provide a promising solution for this problem because they can operate on both numerical (continuous) and categorical (discrete) data sources, and they do not require any assumptions about neither the distributions nor the independence of attribute values. This is especially important for the fusion of measurements from different sources like spectral data, DEM data and other ancillary GIS data. Furthermore, using surrogate splits provides the capability of dealing with missing data during both training and classification, and enables handling instrument malfunctions or the cases where one or more measurements do not exist for some locations. Quantitative and qualitative performance evaluation showed that decision trees provide powerful tools for modeling both pixel and region contents of images and mining of remote sensing image archives.
      Keywords
      Data fusion
      Decision tree classifiers
      Land cover analysis
      Missing data
      Remote sensing
      Artificial intelligence
      Classification (of information)
      Data transfer
      Database systems
      Geographic information systems
      Image analysis
      Image retrieval
      Maximum likelihood estimation
      Pattern recognition
      Remote sensing
      Sensor data fusion
      Decision tree classifiers
      Image archives
      Land cover analysis
      Missing data
      Interactive computer systems
      Permalink
      http://hdl.handle.net/11693/27426
      Published Version (Please cite this version)
      https://doi.org/10.1145/1014052.1016913
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      • Department of Computer Engineering 1413
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