Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability
Abstract
:1. Introduction
Review Scope and Approach
2. Meta-Analysis
2.1. Spectral Range
2.2. Visible (VIS; 400–700 nm)
2.3. Red Edge (680–780 nm)
2.4. Near Infrared (NIR) (700–1327 nm)
2.5. Shortwave Infrared (SWIR) (1328–2500 nm)
2.6. Canopy and Leaf Scale Spectral Selection Rates
3. Feature Selection
3.1. Filter Methods
3.2. Wrapper Methods
3.3. Embedded Methods
3.4. Comparison of Stepwise Discriminant Analysis (SDA) with non-SDA Feature Selectors
4. Study Design Influence
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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References | Wavelengths/Bandwidths | Classes | Pre-Processing | Feature Selection Method | No. Bands Selected | Accuracy % | Study Context and Spatial Scale or Resolution |
---|---|---|---|---|---|---|---|
[22] | 350–1025 nm, 3 nm | 12 | Band depth | Segmented PCA | 12 | 77.0 | Successional plant communities from canopy field spectra |
[23] | 454–950 nm, 4 nm | 8 | Smoothing | SDA | 14 | 91.4 | Mangrove forest field canopy spectra |
[23] | 454–950 nm, 4 nm | 8 | Smoothing | CFS | 23 | 92.3 | Mangrove forest field canopy spectra |
[23] | 454–950 nm, 4 nm | 8 | Smoothing | SPA | 23 | 93.1 | Mangrove forest field canopy spectra |
[10] | 384.8–1054.3 nm, 9.23 nm | 10 | SAM Band Selector Addon | 31 | 53.0 | Savanna tree species from airborne imagery (1.12 m) | |
[11] | 403–989 nm, 4.6 nm | 8 | Sequential Forward Floating Selection | 43 | 74.1 | Alpine tree species and 2 non-species classes, airborne imagery (1 m) | |
[24] | 400–900 nm, 1 nm | 13 | Spec angle and dist., feature parameters | 7 | 96.2 | Varied plant species from lab leaf spectra | |
[25] | 400–1000 nm, 10 nm | 5 | PCA, SDA, Manual selection | 7 | ~91.4 | Crop and weed species from field imagery (1.25 m) | |
[26] | 400–900 nm, 2.6 nm | 25 | Smoothing | Hierarchical Clustering | 13 | 89.0 | Sub-tropical tree species from lab leaf spectra |
[27] | 475–900 nm, 1 nm | 22 | Forward Feature Selection | 8 | 43.0 | Herbaceous wetland species from field leaf spectra | |
[28] | 325–1075 nm, 2 nm | 6 | Smoothing | SDA | 6 | 92.0 | Mangrove forest field canopy spectra |
[28] | 325–1075 nm, 2 nm | 6 | Smoothing, CR | SDA | 17 | 93.6 | Mangrove forest field canopy spectra |
[6] | 400–900 nm, 1.4 nm | 8 | PCA, Discriminant Analysis | 13 | 57.0 | Arid zone plant groups from field leaf spectra | |
[29] | 384.8–1054.3, 9.23 nm | 9 | Random Forest, Gini Index | 8 | 80.3 | Savanna tree species from airborne imagery (1.3 m) | |
[29] | 384.8–1054.3, 9.23 nm | 9 | Continuum removed | Random Forest, Gini Index | 9 | ~79.0 | Savanna tree species from airborne imagery (1.3 m) |
[30] | 393–900 nm, 2.2 nm | 6 | PLSDA VIP score | 78 | 88.8 | Forestry species from airborne imagery (2.4 m) | |
[31] | 400–800 nm, 3 nm | 3 | Two Sample T-test | 5 | 69.1 | Seagrass species field canopy spectra | |
[31] | 400–800 nm, 3 nm | 3 | Normalized | Two Sample T-test | 5 | 66.0 | Seagrass species field canopy spectra |
[31] | 400–800 nm, 3 nm | 3 | Normalized 1st Derivative | Two Sample T-test | 5 | 71.1 | Seagrass species field canopy spectra |
[31] | 400–800 nm, 3 nm | 3 | Normalized 2nd Derivative | Two Sample T-test | 5 | 73.2 | Seagrass species field canopy spectra |
[31] | 400–800 nm, 3 nm | 3 | 1st Derivative | Two Sample T-test | 5 | 69.1 | Seagrass species field canopy spectra |
[31] | 400–800 nm, 3 nm | 3 | 2nd Derivative | Two Sample T-test | 5 | 67.0 | Seagrass species field canopy spectra |
[7] | 400–1000 nm, 3 nm | 13 | Normalisation | PCA, Correlation matrix, Band variance | 53 | 77.0 | European forest trees species from airborne imagery (1.6 m) |
References | Wavelengths/Bandwidths | Classes | Pre-processing | Feature Selection Method | Bands | Accuracy % | Study Context and Spatial Scale or Resolution |
---|---|---|---|---|---|---|---|
[32] | 350–2500 nm @ 3, 10 nm | 4 | ANOVA, CART | 8 | 97.4 | Wetland species from field canopy spectra | |
[33] | 350–2500 nm @ 3, 10 nm | 4 | Resampled | Random Forest | 10 | 90.5 | Wetland species from field canopy spectra |
[9] | 385–2450 nm @ 9.6 nm | 29 | Forward Feature Selection | 7 | 79.2 | Urban street tree species from airborne imagery (3.7 m) | |
[34] | 427–2355 nm @ 10 nm | 4 | PCA | 15 | 86.3 | Agricultural crops, Hyperion (30 m) | |
[35] | 350–2500 nm @ 10nm | 6 | Resampled | ANOVA, LDA | 26 | 77.0 | Mangrove species leaf scale |
[36] | 400–2500 nm @ 16 nm | 16 | Best-First Search Algorithm | 21 | ~69.5 | Temperate forest ecotopes from airborne imagery (4 m) | |
[36] | 400–2500 nm @ 16 nm | 16 | Random Forest | 21 | ~69.5 | Temperate forest ecotopes from airborne imagery (4 m) | |
[37] | 350–2350 nm @ 1 nm | 14 | ANOVA (Tukey HSD), CART | 17 | 98.0 | Rice genotypes from canopy spectra | |
[38] | 400–2500 nm @ 10 nm | 7 | Resampled | SDA | 12 | 70.4 | Eucalypt forest species from lab leaf spectra |
[38] | 400–2500 nm @ 10 nm | 7 | Resampled, 1st Derivative | SDA | 13 | 72.4 | Eucalypt forest species from lab leaf spectra |
[4] | 350–2500 nm @ 1.4, 2 nm | 7 | PCA | 8 | 84.3 | Cabbage crops and weed species from field canopy spectra | |
[39] | 350–2450 nm @ 3, 10nm | 4 | Kruskal-Wallis post hoc Dunn, CART | 56 | ~95 | Giant Reed and coexisting vegetation from field canopy spectra | |
[40] | 400–2400 nm @ 4, 6 nm | 8 | Smoothing | Stepwise Regression Wrapper | 30 | ~70.0 | Tropical tree species from airborne imagery (1 m) |
[41] | 400–2350 nm @ 10 nm | 6 | Continuum Removed | SDA | 29 | 82.3 | Himalayan forest species from satellite imagery (30 m) |
[42] | 429–2400 nm @ 2 nm | 11 | SDA | 40 | ~98.0 | Canadian forest tree species from lab leaf spectra | |
[42] | 429–2400 nm @ 2 nm | 11 | 1st Derivative | SDA | 40 | ~98.0 | Canadian forest tree species from lab leaf spectra |
[42] | 429–2400 nm @ 2 nm | 11 | 2nd Derivative | SDA | 40 | ~98.0 | Canadian forest tree species from lab leaf spectra |
[5] | 426.5–2355 nm @ 10 nm | 5 | LS-means, SDA, PCA, LL-R2 | 29 | 90.2 | Crop species from satellite imagery (30 m) | |
[5] | 426.5–2355 nm @ 10 nm | 5 | Resampled | LS-means, SDA, PCA, LL-R2 | 21 | 92.0 | Crop species from canopy field spectra |
[43] | 415–2340 nm @ 10 nm | 5 | SDA | 25 | 100 | Amazon tree species from satellite imagery (30 m) | |
[43] | 415–2340 nm @ 10 nm | 5 | SDA | 25 | 100 | Amazon tree species from satellite imagery (30 m) | |
[8] | 400–2400 nm @ 5 nm | 46 | Smoothing, Normalization | PCA | 20 | 82.6 | Tropical wetland species from field leaf spectra |
[8] | 400–2400 nm @ 5 nm | 46 | Smoothing, Normalization | Mann-Whitney U-test | 21 | 86.8 | Tropical wetland species from field leaf spectra |
[8] | 400–2400 nm @ 5 nm | 46 | Smoothing, Normalization | ANOVA | 23 | 83.4 | Tropical wetland species from field leaf spectra |
[8] | 400–2400 nm @ 5 nm | 46 | Smoothing, Normalization | SVM | 20 | 87.1 | Tropical wetland species from field leaf spectra |
[8] | 400–2400 nm @ 5 nm | 46 | Smoothing, Normalization | Random Forest | 20 | 86.1 | Tropical wetland species from field leaf spectra |
[8] | 400–2400 nm @ 5 nm | 46 | Smoothing, Normalization | Random Forest (a) | 20 | 84.8 | Tropical wetland species from field leaf spectra |
[44] | 413–2440 nm @ 0.6, 11 nm | 6 | PCA | 40 | 87.0 | ||
[45] | 400–2500 nm @ 2, 6, 10 nm | 27 | Smoothing, Continuum removed | Mann-Whitney U-test, Manual Selection | 6 | - | Saltmarsh vegetation types from field canopy spectra |
[46] | 350–2500 nm @ 3, 10 nm | 7 | ANOVA – post hoc Tukey-Kramer | 9 | 94.7 | Australian forest species from lab leaf spectra | |
[47] | 390–2360 nm @ 10 nm | 4 | Resampled | PCA, LL-R2, SDA, DGVI | 22 | 97.0 | Crops and savanna cover types from field canopy spectra |
[48] | 350–2350 nm @ 10 nm | 8 | SDA | 20 | 95.0 | Crop types from field canopy spectra | |
[49] | 350–2500 nm @ 3, 10 nm | 16 | Genetic Algorithm | 4 | ~80.0 | Mangrove species from lab leaf spectra | |
[50] | 350–2500 nm @ 3, 10 nm | 16 | Genetic Algorithm | (30*4) | ~80.0 | Mangrove species from lab leaf spectra | |
[51] | 400–2500 nm @ 10 nm | 3 | SDA | 10 | 65.0 | Pine tree species from airborne imagery (3.4 m) | |
[51] | 400–2500 nm @ 10 nm | 3 | 1st Derivative | SDA | 10 | 77.0 | Pine tree species from airborne imagery (3.4 m) |
[51] | 400–2500 nm @ 10 nm | 3 | 2nd Derivative | SDA | 10 | 72. | Pine tree species from airborne imagery (3.4 m) |
[52] | 350–2500 nm @ 10 nm | 3 | Resampled | ANOVA, LDA | 15 | 90.0 | Mangrove species from lab leaf spectra |
Feature Selector | Software Package and Library | Hyperpaprametes |
---|---|---|
SVM | Python 3.6, scikit-learn v0.21.3 | C = 100, class_weight = ‘balanced’, kernel = ‘linear’ |
SDA | Python 3.6, milk v0.6.1 | tolerance = 0.001, significance_in = 0.01, significance_out = 0.01, Metric = ‘Wilk’s Lambda’ |
SFFS | R 3.6.1, varSel v0.1 | Metric = “Jeffries-Matusita distance”, Strategy = "mean" |
RF | Python 3.6, scikit-learn v0.21.3 | n_estimators = 100, criterion = ‘gini’, max_depth = None, min_samples_leaf = 1 |
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Hennessy, A.; Clarke, K.; Lewis, M. Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability. Remote Sens. 2020, 12, 113. https://doi.org/10.3390/rs12010113
Hennessy A, Clarke K, Lewis M. Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability. Remote Sensing. 2020; 12(1):113. https://doi.org/10.3390/rs12010113
Chicago/Turabian StyleHennessy, Andrew, Kenneth Clarke, and Megan Lewis. 2020. "Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability" Remote Sensing 12, no. 1: 113. https://doi.org/10.3390/rs12010113