Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability

Abstract

Hyperspectral sensing, measuring reflectance over visible to shortwave infrared wavelengths, has enabled the classification and mapping of vegetation at a range of taxonomic scales, often down to the species level. Classification with hyperspectral measurements, acquired by narrow band spectroradiometers or imaging sensors, has generally required some form of spectral feature selection to reduce the dimensionality of the data to a level suitable for the construction of a classification model. Despite the large number of hyperspectral plant classification studies, an in-depth review of feature selection methods and resultant waveband selections has not yet been performed. Here, we present a review of the last 22 years of hyperspectral vegetation classification literature that evaluates the overall waveband selection frequency, waveband selection frequency variation by taxonomic, structural, or functional group, and the influence of feature selection choice by comparing such methods as stepwise discriminant analysis (SDA), support vector machines (SVM), and random forests (RF). This review determined that all characteristics of hyperspectral plant studies influence the wavebands selected for classification. This includes the taxonomic, structural, and functional groups of the target samples, the methods, and scale at which hyperspectral measurements are recorded, as well as the feature selection method used. Furthermore, these influences do not appear to be consistent. Moreover, the considerable variability in waveband selection caused by the feature selectors effectively masks the analysis of any variability between studies related to plant groupings. Additionally, questions are raised about the suitability of SDA as a feature selection method, with it producing waveband selections at odds with the other feature selectors. Caution is recommended when choosing a feature selector for hyperspectral plant classification: We recommend multiple methods being performed. The resultant sets of selected spectral features can either be evaluated individually by multiple classification models or combined as an ensemble for evaluation by a single classifier. Additionally, we suggest caution when relying upon waveband recommendations from the literature to guide waveband selections or classifications for new plant discrimination applications, as such recommendations appear to be weakly generalizable between studies.

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

“…Several studies reported the strong correlation between reflectance bands and yield in different crop plants such as alfalfa ( Kayad et al, 2016 ; Feng et al, 2020 ), wheat ( Prey and Schmidhalter, 2019 ), maize ( Lane et al, 2020 ), rice ( Wan et al, 2020 ), and sugarcane ( Verma et al, 2020 ). The visible reflectance bands can be splitted into three main regions, red (650–700 nm), green (495–570 nm), and violet–blue (390–495 nm) ( Hennessy et al, 2020 ). Most studies were emphasized the importance of red spectral bands or the combined use of red and red edge bands as one solid index in predicting the total yield ( Jolly et al, 2005 ; Filippa et al, 2018 ; Lykhovyd, 2020 ; Phan et al, 2020 ; Tiwari and Shukla, 2020 ).…”

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

“…This gives a total of 204,800 combinations to work with, which should be enough to configure a training/testing dataset. Although this high dimensionality could offer potential problems to hyperspectral data processes [65], studies already suggested that maintaining the original data could also outperform feature-selected subsets [66,67]. It is also important to address that, although hyperspectral data is relatively easy to obtain, leaf tissue analysis can be limited.…”

“…As a discussion example, a recent paper collected 500 samples per class with 540 spectral bands and adopted a Cross-Validation method with a dataset considering 200 samples for each validation to demonstrate the importance of the feature selection methods [65]. Regardless, hyperspectral data have a characteristic distinct from most data, which is a high number of bands/wavelengths available to model a given problem.…”

“…As mentioned, though the aforementioned studies did use similar sample sizes of data to train, validate, and test their learners, little information related to hyperspectral wavelengths and machine learning method sample size could be encountered in the literature [65]. Since there is no research focused on evaluating the impact of the training set to model spectral data, a previous comparison regarding two well-known sampling methods was performed.…”

Species Classification in a Tropical Alpine Ecosystem Using UAV-Borne RGB and Hyperspectral Imagery

“…Hennessy et al (2020) [57], in their review on the hyperspectral classification of plants, highlighted the importance of testing multiple feature selection and classification methods due to the high variation in outcomes among studies. Out study supports this statement.…”