Analyzing the spectral variability of tropical tree species using hyperspectral feature selection and leaf optical modeling

Ferreira, Matheus Pinheiro; Grondona, Atílio Efrain Bica; Rolim, Silvia Beatriz Alves; Shimabukuro, Yosio Edemir

doi:10.1117/1.jrs.7.073502

Cited by 16 publications

(23 citation statements)

References 36 publications

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“…Despite this, [3] reported greater interspecific variability in the NIR, particularly at the canopy scale, potentially related to species-specific photon scattering caused by differences in canopy architecture, a result also reported by other studies [68,69]. However, it has been suggested that the importance of the NIR and SWIR in [3] is linked to the time delay between leaf collection and spectral measurement, causing a decrease in water content and affecting waveband importance [58].…”

Section: Near Infrared (Nir) (700-1327 Nm)mentioning

confidence: 59%

“…Of these, the red reflectance minimum, specifically bands near 680 nm has previously been identified as the most commonly selected and critical band centre for crop type discrimination [56]. The continued selection of 680 nm, along with neighbouring bands in later studies has validated the importance of this region amongst agricultural crop studies [5,25,47,48,57], as well as for other vegetation types [10,17,18,26,27,29,30,46,58,59]. In addition to the obvious relationship with chlorophyll, absorption in the red region has been related to anthocyanin content, a foliar pigment responsible for the red colouration in leaves [60], particularly evident in juvenile leaves of certain species [30].…”

Section: Visible (Vis; 400-700 Nm)mentioning

confidence: 99%

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Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability

2020

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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.

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Section: Near Infrared (Nir) (700-1327 Nm)mentioning

confidence: 59%

Section: Visible (Vis; 400-700 Nm)mentioning

confidence: 99%

Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability

2020

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“…A post-hoc Tukey HSD test 23 was carried out after the ANOVA test; the HSD test consists of a statistical multiple pairwise comparisons across each wavenumber to identify the spectral locations in which the classes differ most from one another. The red dots in Figs.…”

Section: Resultsmentioning

confidence: 99%

“…Using the calculated values for the spectral amplitudes, D , a Wilcoxon rank sum test 23 was performed for a quantitative statistical comparison between the among-class and within-class cases. The Wilcoxon’s rank sum test tests for the equality of medians in two populations, in this case, the calculated spectral amplitudes, D , of the two cases, among-classes (i.e.…”

Section: Resultsmentioning

confidence: 99%

“…22 ANOVA along with post-hoc Tukey Honestly Significance Difference (HSD) test was used for a spectral feature selection. 23, 24 A test for the sources of the spectral variability was performed based on a calculated spectral amplitude ( D ) in combination with a Wilcoxon rank sum test. 23, 25 Multivariate analysis methods such as partial least squares discriminate analysis (PLS-DA), and support vector machine discriminate analysis (SVM-DA) were employed for classification of the SERS spectra.…”

Section: Experimental Methodsmentioning

confidence: 99%

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Detection of neuraminidase stalk motifs associated with enhanced N1 subtype influenza A virulence via Raman spectroscopy

Choi

Martin

Tripp

et al. 2015

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Oligonucleotides corresponding to neuraminidase (NA) stalk motifs that have been associated with enhanced influenza virulence have been identified using surface-enhanced Raman spectroscopy (SERS).. 5′-thiolated ssDNA oligonucleotides were immobilized onto a hexadecyltrimethylammonium bromide (CTAB) coated Au nanoparticles (AuNP). Three synthetic RNA sequences corresponding to specific amino acid deletions in the influenza NA stalk region were attached to the CTAB-modified AuNPs. Two of these sequences were specific to sequences with amino acid deletions associated with increased virulence, and one was a low virulence sequence with no amino acid deletions. Hybridization of synthetic matched and mismatched DNA-RNA complexes were detected based on the intrinsic SERS spectra. In addition, this platform was used to analyze RNA sequences isolated from laboratory grown influenza viruses having the NA stalk motif associated with enhanced virulence, including A/WSN/33/H1N1, A/Anhui/1/2005/H5N, and A/Vietnam/1203/2004/H5N1 strains. Multivariate feature selection methods were employed to determine the specific wavenumbers in the Raman spectra that contributed the most information for class discrimination. A one-way analysis of variance (ANOVA) test identified 884 and 1196 wavenumbers as being highly significant in the high and low virulence spectra, respectively (p < 0.01). A post-hoc Tukey Honestly Significance Difference (HSD) test identified the wavenumbers that played a major role in differentiating the DNA-RNA hybrid classes. An estimate of the spectral variability, based on the Wilcoxon rank sum test, found the major source of variation to be predominately between the different classes, and not within the classes, thus confirming that the spectra reflected real class differences and not sampling artifacts. The multivariate classification methods partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) were able to distinguish between different NA stalk-motifs linked to NA-enhanced influenza virus virulence (NA-EIV) with >95% sensitivity and specificity in both synthetic RNA sequences as well as the isolated viral RNA. This study demonstrates the feasibility of SERS for direct identification of influenza NA stalk mutations associated with virulence without sample amplification or labeling.

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Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data

Ferreira

Zortea

Zanotta³

et al. 2016

Remote Sensing of Environment

155

107

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Analyzing the spectral variability of tropical tree species using hyperspectral feature selection and leaf optical modeling

Cited by 16 publications

References 36 publications

Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability

Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability

Detection of neuraminidase stalk motifs associated with enhanced N1 subtype influenza A virulence via Raman spectroscopy

Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data

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