Mapping multi-spectral remote sensing images using rule extraction approach

Authors:
Mu-Chun Su;De-Yuan Huang;Jieh-Haur Chen;Wei-Zhe Lu;L. -C. Tsai;Jia-Zheng Lin
Affiliations:
Department of Computer Science and Information Engineering, National Central University, Jhongli, Taoyuan 32001, Taiwan;Department of Computer Science and Information Engineering, National Central University, Jhongli, Taoyuan 32001, Taiwan;Institute of Construction Engineering and Management, National Central University, Jhongli, Taoyuan 32001, Taiwan;Department of Computer Science and Information Engineering, National Central University, Jhongli, Taoyuan 32001, Taiwan;Center for Space and Remote Sensing Research, National Central University, Jhongli, Taoyuan 32001, Taiwan;Institute of Construction Engineering and Management, National Central University, Jhongli, Taoyuan 32001, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

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.