Mapping multi-spectral remote sensing images using rule extraction approach

Mu Chun Su, De Yuan Huang, Jieh Haur Chen, Wei Zhe Lu, L. C. Tsai, Jia Zheng Lin

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


To improve the accurate rate of mapping multi-spectral remote sensing images, in this paper we construct a class of HyperRectangular Composite Neural Networks (HRCNNs), integrating the paradigms of neural networks with the rule-based approach. The supervised decision-directed learning (SDDL) algorithm is also adopted to construct a two-layer network in a sequential manner by adding hidden nodes as needed. Thus, the classification knowledge embedded in the numerical weights of trained HRCNNs can be extracted and represented in the form of If-Then rules. The rules facilitate justification on the responses to increase accuracy of the classification. A sample of remote sensing image containing forest land, river, dam area, and built-up land is used to examine the proposed approach. The accurate recognition rate reaching over 99% demonstrates that the proposed approach is capable of dealing with image mapping.

Original languageEnglish
Pages (from-to)12917-12922
Number of pages6
JournalExpert Systems with Applications
Issue number10
StatePublished - 15 Sep 2011


  • Fuzzy systems
  • Image classification
  • Neural networks
  • Remote sensing
  • Rule extraction


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