A rough set approach to the discovery of classification rules in spatial data

  • Authors:

  • Yee Leung;Tung Fung;Ju-Sheng Mi;Wei-Zhi Wu

  • Affiliations:

  • Department of Geography and Resource Management, Center for Environmental Policy and Resource Management, and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, ...;Department of Geography and Resource Management, Center for Environmental Policy and Resource Management, and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, ...;College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, Hebei, P. R. China;Information College, Zhejiang Ocean University, Zhoushan, Zhejiang, P. R. China

  • Venue:
  • International Journal of Geographical Information Science
  • Year:
  • 2007

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Abstract

This paper proposes a novel rough set approach to discover classification rules in real-valued spatial data in general and remotely sensed data in particular. A knowledge induction process is formulated to select optimal decision rules with a minimal set of features necessary and sufficient for a remote sensing classification task. The approach first converts a real-valued or integer-valued decision system into an interval-valued information system. A knowledge induction procedure is then formulated to discover all classification rules hidden in the information system. Two real-life applications are made to verify and substantiate the conceptual arguments. It demonstrates that the proposed approach can effectively discover in remotely sensed data the optimal spectral bands and optimal rule set for a classification task. It is also capable of unraveling critical spectral band(s) discerning certain classes. The framework paves the road for data mining in mixed spatial databases consisting of qualitative and quantitative data.