A NOVEL INTEGRATED PCA AND FLD METHOD ON HYPERSPECTRAL IMAGE FEATURE EXTRACTION FOR CUCUMBER CHILLING DAMAGE INSPECTION

Abstract

High-resolution hyperspectral imaging (HSI) provides an abundance of spectral data for feature analysis in imageprocessing. Usually, the amount of information contained in hyperspectral images is excessive and redundant, and data miningfor waveband selection is needed. In applications such as fruit and vegetable defect inspections, effective spectral combinationand data fusing methods are required in order to select a few optimal wavelengths without losing the crucialinformation in the original hyperspectral data. In this article, we present a novel method that combines principal componentanalysis (PCA) and Fishers linear discriminant (FLD) method to show that the hybrid PCA-FLD method maximizes the representationand classification effects on the extracted new feature bands. The method is applied to the detection of chillinginjury on cucumbers. Based on tests on different types of samples, results show that this new integrated PCA-FLD methodoutperforms the PCA and FLD methods when they are used separately for classifications. This method adds a new tool forthe multivariate analysis of hyperspectral images and can be extended to other hyperspectral imaging applications for fruitand vegetable safety and quality inspections.