Evaluating the Performance of a Random Forest Kernel for Land Cover Classification
"> Figure 1
<p>Example of general design of RF classifier with <span class="html-italic">n</span> number of trees.</p> "> Figure 2
<p>Example of a linear (<b>a</b>) and a nonlinear SVM (<b>b</b>) for a two-class classification problem. The nonlinear SVM maps the data into high dimensional space to separate linearly the classes of the data.</p> "> Figure 3
<p>(<b>a</b>) study area of Sukumba site, southeast of Koutiala, Mali; (<b>b</b>) crop polygons for Mali and (<b>c</b>) study area of Salinas Valley, CA, USA and (<b>d</b>) RGB composite of Salinas.</p> "> Figure 4
<p>Overview of the steps followed to compare SVM-RFK with RF and SVM-RBF. Notation: The boxes with Sukumba dataset indicate steps that were only applied to this dataset, and the rest of the boxes indicate steps applied to both datasets.</p> "> Figure 5
<p>Comparison of <math display="inline"> <semantics> <mover> <mrow> <mi>O</mi> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </semantics> </math> and <math display="inline"> <semantics> <mover accent="true"> <mi>κ</mi> <mo>¯</mo> </mover> </semantics> </math> obtained for RF, SVM-RBF, and SVM-RFK classifiers. Notation: <math display="inline"> <semantics> <mover> <mrow> <mi>O</mi> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </semantics> </math> (in %) is the overall accuracy averaged over 10 test samples, <math display="inline"> <semantics> <mover accent="true"> <mi>κ</mi> <mo>¯</mo> </mover> </semantics> </math> is the Cohen’s kappa index averaged over 10 test samples, and the standard deviations for <math display="inline"> <semantics> <mrow> <mi>O</mi> <mi>A</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mi>κ</mi> </semantics> </math> values are shown with error bars. RF and SVM-RFK denote classifiers created with an optimized <span class="html-italic">mtry</span> value, and RF<math display="inline"> <semantics> <msub> <mrow/> <mi>d</mi> </msub> </semantics> </math> and SVM-RFK<math display="inline"> <semantics> <msub> <mrow/> <mi>d</mi> </msub> </semantics> </math> denote classifiers created with the default <span class="html-italic">mtry</span> value.</p> "> Figure 6
<p>Classification time required by SVM classifiers.</p> "> Figure 7
<p>RBF Kernels (<b>top</b>) and RFKs (<b>bottom</b>) for the datasets from left to right: Salinas (Spectral features), Sukumba (Spectral features), and Sukumba (Spectral features and additional features). Class labels are shown on the bottom of the kernels. The class labels go from 1 to 5 for Sukumba, and from 1 to 16 for Salinas.</p> "> Figure 8
<p>RF Kernel for top 100 features selected by RF (out of 1057). Class labels are shown on the bottom of the kernel. The class labels go from 1 to 5 for Sukumba.</p> "> Figure 9
<p>Two crop classified fields per ground truth class along with the overall accuracy for the different classifiers using spectral features, and the top 100 features for SVM-RFK-MIF. The trees within the crops were excluded from the classification (masked, unclassified).</p> "> Figure 10
<p>Ground truth and three classification maps (and the OA (%) calculated using all the pixels in the dataset on the top) for the RF, SVM-RBF, and SVM-RFK classifiers using the AVIRIS spectral features.</p> ">
Abstract
:1. Introduction
2. Methods
2.1. Random Forest
2.2. Support Vector Machine
2.3. Random Forest Kernel
3. Data and Ground Truth
3.1. WorldView-2
3.2. AVIRIS
4. Preprocessing and Experimental Set-Up
4.1. Preprocessing
4.2. Experimental Set-Up
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AVIRIS | Airborne Visible Infrared Imaging Spectrometer |
DVI | Difference Vegetation Index |
EVI | Enhanced Vegetation Index |
ML | Maximum Likelihood |
MIF | Most Important Features |
MSAVI2 | Modified Soil-Adjusted Vegetation Index |
NDVI | Normalized Vegetation Index |
NN | Neural Networks |
PRI | Photochemical Reflectance Index |
OA | Overall Accuracy |
OSAVI | Optimized Soil Adjusted Vegetation Index |
RBF | Radial Basis Function |
SVM-RBF | Radial Basis Function Support Vector Machine classifier |
RF | Random Forest |
RF-BD | Best Depth Random Forest Classifier |
RF-FG | Full Grown Random Forest Classifier |
RFK | Random Forest Kernel |
RFK-BD-SVM | Best Depth Random Forest Kernel Support Vector Machine Classifier |
RFK-FG-SVM | Full Grown Random Forest Kernel Support Vector Machine Classifier |
RGB | Red, Green and Blue Color |
RS | Remote Sensing |
RKHS | Reproducing Kernel Hilbert Space |
RVI | Ratio-Based Vegetation Indices |
SAVI | Soil Adjusted Vegetation Index |
SD | Standard Deviation |
SVM | Support Vector Machine |
TCARI | Transformed Chlorophyll Absorption Reflectance Index |
VI | Vegetation Index |
WBI | Water Band Index |
WV2 | WorldView-2 |
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Dataset | Features | ||||
---|---|---|---|---|---|
Sukumba | Spectral features | 56 | 2043 | 1858 | 5 |
Spectral & additional features | 1057 | ||||
Salinas | Spectral features | 204 | 24612 | 20782 | 16 |
Formula | Description |
---|---|
NDVI is a proxy for the amount of vegetation, and helps to distinguish the vegetation from the soil while minimizing the topographic effects, though does not eliminate the atmospheric effects [51]. | |
DVI also helps to distinguish between soil and vegetation, yet does not deal with the difference between the reflectance and radiance from atmosphere or shadows [52] | |
RVI is the simplest ratio-based index showing high values for the vegetation and low values for soil, ice, water, etc. This index can reduce the atmospheric and topographic effects [52]. | |
SAVI is similar to the NDVI, yet it suppresses the soil effects by using an adjustment factor, L, which is a vegetation canopy background adjustment factor. L varies from 0 to 1 and often requires prior knowledge of vegetation densities to be set [53]. | |
MSAVI is a developed version of SAVI where the L-factor dynamically is adjusted using the image data and MSAVI2 is an iterated version of MSAVI [54]. | |
TCARI indicates the relative abundance of chlorophyll using the reflectance at the wavelengths of 700 (i.e., R700), 670 and 550 and reduces the background (soil and non-photosynthetic components) effects compared to the initial versions of this index [55]. | |
EVI is developed to improve the NDVI by optimizing the vegetation signal with using blue reflectance to correct the soil background and atmospheric influences [56]. |
Tests | Methods | SD | SD | ||
---|---|---|---|---|---|
Sukumba | |||||
RF | 81.08 | 1.34 | 0.76 | 0.02 | |
RF | 80.64 | 0.98 | 0.75 | 0.01 | |
Spectral features | SVM-RBF | 82.08 | 2.21 | 0.77 | 0.03 |
SVM-RFK | 81.34 | 1.27 | 0.76 | 0.02 | |
SVM-RFK | 80.68 | 1.12 | 0.75 | 0.01 | |
Spectral features and additional features | RF | 80.82 | 1.31 | 0.76 | 0.02 |
RF | 80.46 | 1.20 | 0.75 | 0.01 | |
SVM-RBF | 77.96 | 1.26 | 0.72 | 0.02 | |
SVM-RFK | 82.30 | 1.02 | 0.77 | 0.01 | |
SVM-RFK | 82.14 | 0.84 | 0.77 | 0.01 | |
Salinas | |||||
RF | 94.16 | 0.5 | 0.93 | 0.004 | |
RF | 94.10 | 0.48 | 0.93 | 0.005 | |
Spectral features | SVM-RBF | 95.83 | 0.52 | 0.95 | 0.01 |
SVM-RFK | 94.42 | 0.56 | 0.94 | 0.005 | |
SVM-RFK | 94.38 | 0.47 | 0.94 | 0.005 |
Methods | SD | SD | ||
---|---|---|---|---|
RF-MIF | 79.68 | 1.31 | 0.74 | 0.01 |
SVM-RFK-MIF | 84.96 | 1.66 | 0.81 | 0.02 |
Kernels | Sukumba: Sp | Sukumba: Sp&Ad | Salinas |
---|---|---|---|
RFK | 0.016 | 0.021 | 0.041 |
RFK_d | 0.018 | 0.021 | 0.042 |
RBF | 0.010 | 0.004 | 0.029 |
Test | Classes | RF | RF | SVM-RBF | SVM-RFK | SVM-RFK | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
SD | SD | SD | SD | SD | |||||||
Maize | 0.78 | 0.03 | 0.77 | 0.025 | 0.80 | 0.02 | 0.78 | 0.02 | 0.76 | 0.02 | |
Millet | 0.86 | 0.02 | 0.85 | 0.02 | 0.87 | 0.03 | 0.85 | 0.02 | 0.84 | 0.02 | |
Spectral features | Peanut | 0.78 | 0.02 | 0.78 | 0.02 | 0.79 | 0.04 | 0.79 | 0.02 | 0.77 | 0.01 |
Sorghum | 0.84 | 0.02 | 0.84 | 0.009 | 0.86 | 0.02 | 0.86 | 0.02 | 0.84 | 0.01 | |
Cotton | 0.79 | 0.02 | 0.79 | 0.02 | 0.79 | 0.03 | 0.80 | 0.02 | 0.79 | 0.02 | |
Spectral features and additional features | Maize | 0.77 | 0.04 | 0.76 | 0.03 | 0.75 | 0.03 | 0.77 | 0.03 | 0.76 | 0.02 |
Millet | 0.85 | 0.02 | 0.84 | 0.01 | 0.83 | 0.02 | 0.87 | 0.02 | 0.86 | 0.01 | |
Peanut | 0.80 | 0.02 | 0.79 | 0.02 | 0.77 | 0.02 | 0.82 | 0.02 | 0.81 | 0.01 | |
Sorghum | 0.82 | 0.02 | 0.82 | 0.02 | 0.81 | 0.03 | 0.84 | 0.02 | 0.84 | 0.02 | |
Cotton | 0.80 | 0.02 | 0.80 | 0.02 | 0.73 | 0.02 | 0.82 | 0.02 | 0.83 | 0.01 |
Test | Classes | RF | RF | SVM-RBF | SVM-RFK | SVM-RFK | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
SD | SD | SD | SD | SD | |||||||
1:Brocoli_1 | 1.00 | 0.008 | 1.00 | 0.007 | 1.00 | 0.005 | 1.00 | 0.005 | 1.00 | 0.007 | |
2:Brocoli_2 | 0.99 | 0.009 | 0.99 | 0.009 | 1.00 | 0.005 | 1.00 | 0.006 | 0.99 | 0.007 | |
3:Fallow | 0.97 | 0.018 | 0.97 | 0.017 | 0.98 | 0.012 | 0.97 | 0.014 | 0.97 | 0.015 | |
4:Fallow_rough | 0.99 | 0.008 | 0.99 | 0.008 | 0.99 | 0.007 | 0.99 | 0.007 | 0.99 | 0.007 | |
5:Fallow_smooth | 0.98 | 0.010 | 0.98 | 0.009 | 0.99 | 0.012 | 0.98 | 0.010 | 0.98 | 0.011 | |
6:Stubble | 1.00 | 0.003 | 1.00 | 0.003 | 1.00 | 0.002 | 1.00 | 0.004 | 1.00 | 0.005 | |
7:Celery | 0.99 | 0.006 | 0.99 | 0.005 | 1.00 | 0.004 | 0.99 | 0.007 | 0.99 | 0.007 | |
Spectral features | 8:Grapes_untr. | 0.69 | 0.032 | 0.69 | 0.039 | 0.76 | 0.026 | 0.70 | 0.042 | 0.69 | 0.041 |
9:Soil_Vineyard | 0.99 | 0.009 | 0.98 | 0.009 | 0.99 | 0.006 | 0.99 | 0.007 | 0.99 | 0.007 | |
10:Corn | 0.91 | 0.011 | 0.91 | 0.014 | 0.94 | 0.019 | 0.91 | 0.009 | 0.91 | 0.009 | |
11:Lettuce_4wk | 0.96 | 0.011 | 0.96 | 0.008 | 0.98 | 0.010 | 0.97 | 0.011 | 0.97 | 0.011 | |
12:Lettuce_5wk | 0.98 | 0.010 | 0.98 | 0.011 | 0.98 | 0.008 | 0.98 | 0.011 | 0.98 | 0.010 | |
13:Lettuce_6wk | 0.97 | 0.012 | 0.97 | 0.011 | 0.99 | 0.010 | 0.98 | 0.012 | 0.98 | 0.012 | |
14:Lettuce_7wk | 0.95 | 0.018 | 0.95 | 0.018 | 0.98 | 0.014 | 0.96 | 0.016 | 0.96 | 0.017 | |
15:Vineyard_untr. | 0.71 | 0.036 | 0.72 | 0.045 | 0.76 | 0.033 | 0.71 | 0.051 | 0.71 | 0.044 | |
16:Vineyard_vertical | 0.98 | 0.013 | 0.98 | 0.014 | 0.99 | 0.006 | 0.98 | 0.013 | 0.98 | 0.012 |
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Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E. Evaluating the Performance of a Random Forest Kernel for Land Cover Classification. Remote Sens. 2019, 11, 575. https://doi.org/10.3390/rs11050575
Zafari A, Zurita-Milla R, Izquierdo-Verdiguier E. Evaluating the Performance of a Random Forest Kernel for Land Cover Classification. Remote Sensing. 2019; 11(5):575. https://doi.org/10.3390/rs11050575
Chicago/Turabian StyleZafari, Azar, Raul Zurita-Milla, and Emma Izquierdo-Verdiguier. 2019. "Evaluating the Performance of a Random Forest Kernel for Land Cover Classification" Remote Sensing 11, no. 5: 575. https://doi.org/10.3390/rs11050575