Spectral Correction and Dimensionality Reduction of Hyperspectral Images for Plant Water Stress Assessment

Abstract

Hyperspectral Imaging (HSI) is one of the emerging techniques used in plant phenotyping as it carries abundant information and is non-invasive to plants. However, factors like illumination effect and high-dimensional spectral features need to be solved to attain higher accuracy of plant trait analysis. This research explored and analysed spectral normalisation and dimensionality reduction methods. The focus of this paper is twofold; the first objective was to explore the Standard Normal Variate (SNV), Least Absolute Deviations (L1) and Least Squares (L2) normalisation for spectral correction. The second objective was to explore the feasibility of Principal Component Analysis (PCA) and Analysis of Variance Fisher’s Test (ANOVA F-test) for spectral dimensionality reduction in spectral discriminative modelling. The analysis techniques were validated with HSI data of maise plants for early detection of water deficit stress response. Results showed that SNV performed the best among the three normalisation methods. Besides, ANOVA F-test outperformed PCA for the band selection method as it improved the trait assessment on the water deficit response of maise plants.