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Proceedings Paper

Learning object filters for high-resolution satellite images using genetic algorithms
Author(s): Hong Zheng; Saeid Nahavandi; Li Pan
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Paper Abstract

This paper introduces a novel methodology for texture object detection using genetic algorithms. The method employs a kind of high performance detection filter defined as 2D masks, which are derived using genetic algorithm operating. The population of filters iteratively evaluated according to a statistical performance index corresponding to object detection ability, and evolves into an optimal filter using the evolution principles of genetic search. Experimental results of texture object detection in high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method.

Paper Details

Date Published: 11 June 2003
PDF: 7 pages
Proc. SPIE 4898, Image Processing and Pattern Recognition in Remote Sensing, (11 June 2003); doi: 10.1117/12.467301
Show Author Affiliations
Hong Zheng, Deakin Univ. (Australia)
Saeid Nahavandi, Deakin Univ. (Australia)
Li Pan, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 4898:
Image Processing and Pattern Recognition in Remote Sensing
Stephen G. Ungar; Shiyi Mao; Yoshifumi Yasuoka, Editor(s)

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