Feature selection of hyperspectral data by considering the integration of genetic algorithms and particle swarm optimization

Pedram Ghamisi; Jon Atli Benediktsson

doi:10.1117/12.2065472

23 October 2014 Feature selection of hyperspectral data by considering the integration of Genetic Algorithms and Particle Swarm Optimization

Pedram Ghamisi, Jon Atli Benediktsson

Author Affiliations +

Proceedings Volume 9244, Image and Signal Processing for Remote Sensing XX; 92440J (2014) https://doi.org/10.1117/12.2065472
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands

Abstract

At this stage of data acquisition, we are in the era of massive automatic data collection, systematically obtaining many measurements, not knowing which data are appropriate for a problem at hand. In this paper, a feature selection approach is discussed. The approach is based on the integration of a Genetic Algorithm and Particle Swarm Optimization. Support Vector Machine classifier is used as fitness function and its corresponding overall accuracy on validation samples is used as fitness value, in order to evaluate the efficiency of different groups of bands. The approach is carried out on the wellknown Salinas hyperspectral data set. Results confirm that the new approach is able to automatically select the most informative features in terms of classification accuracy within an acceptable CPU processing time without requiring the number of desired features to be set a priori by users.

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Pedram Ghamisi and Jon Atli Benediktsson "Feature selection of hyperspectral data by considering the integration of Genetic Algorithms and Particle Swarm Optimization", Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 92440J (23 October 2014); https://doi.org/10.1117/12.2065472

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