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      Land cover classification with multi-sensor fusion of partly missing data

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      Author(s)
      Aksoy, S.
      Koperski, K.
      Tusk, C.
      Marchisio, G.
      Date
      2009-05
      Source Title
      Photogrammetric Engineering and Remote Sensing
      Print ISSN
      0099-1112
      Publisher
      American Society for Photogrammetry and Remote Sensing
      Volume
      75
      Issue
      5
      Pages
      577 - 593
      Language
      English
      Type
      Article
      Item Usage Stats
      146
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      95
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      Abstract
      We describe a system that uses decision tree-based tools for seamless acquisition of knowledge for classification of remotely sensed imagery. We concentrate on three important problems in this process: information fusion, model understandability, and handling of missing data. Importance of multi-sensor information fusion and the use of decision tree classifiers for such problems have been well-studied in the literature. However, these studies have been limited to the cases where all data sources have a full coverage for the scene under consideration. Our contribution in this paper is to show how decision tree classifiers can be learned with alternative (surrogate) decision nodes and result in models that are capable of dealing with missing data during both training and classification to handle cases where one or more measurements do not exist for some locations. We present detailed performance evaluation regarding the effectiveness of these classifiers for information fusion and feature selection, and study three different methods for handling missing data in comparative experiments. The results show that surrogate decisions incorporated into decision tree classifiers provide powerful models for fusing information from different data layers while being robust to missing data. © 2009 American Society for Photogrammetry and Remote Sensing.
      Keywords
      Comparative experiments
      Data layer
      Data source
      Decision tree classifiers
      Feature selection
      Land cover classification
      Missing data
      Multi-sensor fusion
      Multi-sensor information fusion
      Performance evaluation
      Remotely sensed imagery
      Understandability
      Classifiers
      Data handling
      Decision trees
      Feature extraction
      Information fusion
      Learning systems
      Remote sensing
      Sensor data fusion
      Comparative study
      Image classification
      Land cover
      Modeling
      Sensor
      Permalink
      http://hdl.handle.net/11693/22768
      Published Version (Please cite this version)
      https://doi.org/10.14358/PERS.75.5.577
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      • Department of Computer Engineering 1435
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