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Proceedings Paper

Neural network classification with optimization by genetic algorithms for remote sensing imagery
Author(s): Xiaohua Tong; Xue Zhang
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Paper Abstract

On the remote sensing imagery classification, the traditional methods based on statistical principle has the difficulties in distinguishing the objects with similar spectral characteristics, while the back propagation neural network method has the difficulties in sufficiency and convergence. Therefore, a new method based on neural network classification with optimization by genetic algorithms for remote sensing imagery is proposed in this paper. On the basis of the back propagation( BP )neural network classification, the optimization method by genetic algorithms is presented, including the numbers, the thresholds and the connection weights of nerve nodes of the hide layer in BP neural network. An approach on float coding with alterative length for genetic algorithms is proposed, and the evolution method is improved to obtain an optimal BP neural network. In the end, an experimental test on the remote sensing classification using TM image of Dianshan Lake is carried out, and higher classification accuracy has obtained compared to other methods, which is proved the feasibility and validity of the proposed approach.

Paper Details

Date Published: 26 July 2007
PDF: 13 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67522K (26 July 2007); doi: 10.1117/12.760763
Show Author Affiliations
Xiaohua Tong, Tongji Univ. (China)
Xue Zhang, Tongji Univ. (China)


Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information

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