Learning object filters for high-resolution satellite images using genetic algorithms

Hong Zheng; Saeid Nahavandi; Li Pan

doi:10.1117/12.467301

11 June 2003 Learning object filters for high-resolution satellite images using genetic algorithms

Hong Zheng, Saeid Nahavandi, Li Pan

Proceedings Volume 4898, Image Processing and Pattern Recognition in Remote Sensing; (2003) https://doi.org/10.1117/12.467301
Event: Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2002, Hangzhou, China

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.

Citation Download Citation

Hong Zheng, Saeid Nahavandi, and Li Pan "Learning object filters for high-resolution satellite images using genetic algorithms", Proc. SPIE 4898, Image Processing and Pattern Recognition in Remote Sensing, (11 June 2003); https://doi.org/10.1117/12.467301

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