A new hybridized dimensionality reduction approach using genetic algorithm and folded linear discriminant analysis applied to hyperspectral imaging for effective rice seed classification

Hyperspectral imaging (HSI) has been reported to produce promising results in the classification of rice seeds. However, HSI data often require the use of dimensionality reduction techniques for the removal of redundant data. Folded linear discriminant analysis (F-LDA) is an extension of linear discriminant analysis (LDA, a commonly used technique for dimensionality reduction), and was recently proposed to address the limitations of LDA, particularly its poor performance when dealing with a small number of training samples which is a usual scenario in HSI applications. This article presents an improved version of F-LDA, exploring the feasibility of hybridizing a genetic algorithm (GA) and F-LDA for effective dimensionality reduction in HSI-based rice seeds classification. The proposed approach, inspired by the previous combination of GA with principle component analysis, is evaluated on rice seed datasets containing 256 spectral bands. Experimental results show that, in addition to attaining promising classification accuracies of up to 96.21%, this novel combination of GA and F-LDA (GA + F-LDA) can further reduce the computational complexity and memory requirement in the standalone F-LDA. It is worth noting that these benefits are not without a slight reduction in classification accuracy when evaluated against those reported for the standard F-LDA (up to 96.99%)

A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

Hyperspectral imaging (HSI) has been reported to produce promising results in the classification of rice seeds. However, HSI data often require the use of dimensionality reduction techniques for the removal of redundant data. Folded linear discriminant analysis (F-LDA) is an extension of linear discriminant analysis (LDA, a commonly used technique for dimensionality reduction), and was recently proposed to address the limitations of LDA, particularly its poor performance when dealing with a small number of training samples which is a usual scenario in HSI applications. This article presents an improved version of F-LDA, exploring the feasibility of hybridizing a genetic algorithm (GA) and F-LDA for effective dimensionality reduction in HSI-based rice seeds classification. The proposed approach, inspired by the previous combination of GA with principle component analysis, is evaluated on rice seed datasets containing 256 spectral bands. Experimental results show that, in addition to attaining promising classification accuracies of up to 96.21%, this novel combination of GA and F-LDA (GA + F-LDA) can further reduce the computational complexity and memory requirement in the standalone F-LDA. It is worth noting that these benefits are not without a slight reduction in classification accuracy when evaluated against those reported for the standard F-LDA (up to 96.99%)