Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Classification Scheme
3.2. Input Data
3.3. Training and Test Data
3.4. Algorithms
3.4.1. Random Forest (RF)
3.4.2. Support Vector Machine (SVM)
3.5. Accuracy Assessment
4. Results
5. Discussion
5.1. Effectiveness and Efficiency of the Algorithms
5.2. Impact of Parameter Tuning
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Land Cover Class | Class Definition Based on UN LCCS |
---|---|
1. Bare/Sparse vegetation | Barren land or surface covered by plants that should not exceed 10% in every season of the year. |
2. Built-up | Area of land covered with buildings and other man-made features. |
3. Cropland | Intermittently cultivated land that is harvested and then left fallow (e.g., single and multiple cropping systems). Perennial woody crops are classified as either forest or shrub according to the criteria. |
4. Forest | Woody plants cover more than 15% of the land and grow to a height of more than 5 m. Exceptions: even if its height is less than 5 m but larger than 3 m, a woody plant with a characteristic physiognomic trait of a tree can be classified as a tree. |
5. Herbaceous wetland | A persistent mixture of water and herbaceous or woody vegetation covers the land. The plants can exist in salt, brackish, or freshwater. |
6. Herbaceous vegetation | Plants with no persistent branches or shoots above the surface and no apparent solid structure. Up to 10% of the area may be comprised of trees and plants. |
7. Shrubs | Woody perennial plants with persistent and woody stems that are less than 5 m tall and do not have a clear main stem. The shrub’s leaves are either evergreen or deciduous. |
8. Water bodies | These include lakes, reservoirs, and rivers. The water could be fresh or brine. |
Band No. | Name of Bands | Wavelength Range in μm |
---|---|---|
Band 1 | FY-3C_VIRR_Day_EV_RefSB | 0.58–0.68 |
Band 2 | FY-3C_VIRR_Day_EV_RefSB | 0.84–0.89 |
Band 3 | FY-3C_VIRR_Day_EV_RefSB | 1.55–1.64 |
Band 4 | FY-3C_VIRR_Day_EV_RefSB | 0.43–0.48 |
Band 5 | FY-3C_VIRR_Day_EV_RefSB | 0.48–0.53 |
Band 6 | FY-3C_VIRR_Day_EV_RefSB | 0.53–0.58 |
Band 7 | FY-3C_VIRR_Day_EV_RefSB | 1.325–1.395 |
Band 8 | FY-3C_VIRR_Day_EV_Emissive | 3.55–3.93 |
Band 9 | FY-3C_VIRR_Day_EV_Emissive | 10.3–11.3 |
Band 10 | FY-3C_VIRR_Day_EV_Emissive | 11.5–12.5 |
Band 11 | MVC value of NDVI | - |
Class (Name) | Training Data | Test Data |
---|---|---|
Number of Pixels per Class | ||
Class 1 (Bare/Sparse vegetation) | 20,585 | 6424 |
Class 2 (Built-up) | 1620 | 575 |
Class 3 (Cropland) | 15,472 | 4246 |
Class 4 (Forest) | 11,378 | 3101 |
Class 5 (Herbaceous wetland) | 1686 | 602 |
Class 6 (Herbaceous vegetation) | 12,394 | 3641 |
Class 7 (Shrubs) | 18,956 | 5283 |
Class 8 (Water bodies) | 9116 | 3795 |
Total number of pixels | 91,207 | 27,667 |
Experiment No. | Input Data | Class Name | Bare/Sparse Vegetation | Built-Up | Cropland | Forest | Herbaceous Wetland | Herbaceous Vegetation | Shrub | Water Bodies |
---|---|---|---|---|---|---|---|---|---|---|
Class ID | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||
No. of Pixels | 6424 | 575 | 4246 | 3101 | 602 | 3641 | 5283 | 3795 | ||
1 | Parameter | Ntree | default (100) | |||||||
Mtry | default (auto, i.e., is the square root of no. variables) | |||||||||
Accuracy measures | precision | 0.95 | 0.69 | 0.81 | 0.91 | 0.74 | 0.81 | 0.77 | 0.93 | |
recall | 0.93 | 0.57 | 0.87 | 0.87 | 0.68 | 0.71 | 0.82 | 0.98 | ||
f1-score | 0.94 | 0.63 | 0.84 | 0.89 | 0.71 | 0.76 | 0.80 | 0.95 | ||
OA | 0.86 (0.857) | |||||||||
k | 0.83 (0.829) | |||||||||
2 | Parameter | Ntree | 300 | |||||||
Mtry | default | |||||||||
Accuracy measures | precision | 0.95 | 0.63 | 0.81 | 0.91 | 0.73 | 0.81 | 0.77 | 0.93 | |
recall | 0.92 | 0.57 | 0.87 | 0.86 | 0.71 | 0.71 | 0.82 | 0.98 | ||
f1-score | 0.93 | 0.60 | 0.84 | 0.88 | 0.72 | 0.76 | 0.80 | 0.95 | ||
OA | 0.85 (0.854) | |||||||||
k | 0.83 (0.825) | |||||||||
3 | Parameter | Ntree | 500 | |||||||
Mtry | default | |||||||||
Accuracy measures | precision | 0.95 | 0.67 | 0.81 | 0.91 | 0.73 | 0.82 | 0.77 | 0.93 | |
recall | 0.92 | 0.58 | 0.87 | 0.85 | 0.70 | 0.72 | 0.83 | 0.98 | ||
f1-score | 0.93 | 0.62 | 0.84 | 0.88 | 0.72 | 0.76 | 0.80 | 0.95 | ||
OA | 0.86 (0.856) | |||||||||
k | 0.83 (0.828) | |||||||||
4 | Parameter | Ntree | 700 | |||||||
Mtry | default | |||||||||
Accuracy measures | precision | 0.95 | 0.72 | 0.81 | 0.90 | 0.74 | 0.82 | 0.77 | 0.93 | |
recall | 0.93 | 0.57 | 0.87 | 0.85 | 0.69 | 0.72 | 0.83 | 0.98 | ||
f1-score | 0.94 | 0.64 | 0.84 | 0.88 | 0.71 | 0.76 | 0.80 | 0.95 | ||
OA | 0.86 (0.856) | |||||||||
k | 0.83 (0.858) | |||||||||
5 | Parameter | Ntree | 1000 | |||||||
Mtry | default | |||||||||
Accuracy measures | precision | 0.95 | 0.70 | 0.81 | 0.91 | 0.73 | 0.82 | 0.77 | 0.93 | |
recall | 0.93 | 0.57 | 0.86 | 0.85 | 0.68 | 0.71 | 0.83 | 0.98 | ||
f1-score | 0.94 | 0.63 | 0.83 | 0.88 | 0.71 | 0.76 | 0.80 | 0.95 | ||
OA | 0.86 (0.856) | |||||||||
k | 0.83 (0.827) | |||||||||
6 | Parameter | Ntree | default (100) | |||||||
Mtry | 10 | |||||||||
Accuracy measures | precision | 0.95 | 0.66 | 0.78 | 0.89 | 0.75 | 0.80 | 0.77 | 0.94 | |
recall | 0.92 | 0.55 | 0.86 | 0.85 | 0.66 | 0.70 | 0.82 | 0.98 | ||
f1-score | 0.93 | 0.60 | 0.82 | 0.87 | 0.70 | 0.75 | 0.79 | 0.96 | ||
OA | 0.85 | |||||||||
k | 0.82 | |||||||||
7 | Parameter | Ntree | default (100) | |||||||
Mtry | 40 | |||||||||
Accuracy measures | precision | 0.94 | 0.48 | 0.80 | 0.92 | 0.71 | 0.81 | 0.76 | 0.94 | |
recall | 0.89 | 0.57 | 0.86 | 0.85 | 0.67 | 0.70 | 0.83 | 0.98 | ||
f1-score | 0.92 | 0.52 | 0.83 | 0.88 | 0.69 | 0.75 | 0.80 | 0.96 | ||
OA | 0.85 | |||||||||
k | 0.82 | |||||||||
8 | Parameter | Ntree | default (100) | |||||||
Mtry | 100 | |||||||||
Accuracy measures | precision | 0.94 | 0.53 | 0.79 | 0.91 | 0.68 | 0.79 | 0.76 | 0.91 | |
recall | 0.88 | 0.58 | 0.85 | 0.84 | 0.62 | 0.71 | 0.83 | 0.98 | ||
f1-score | 0.91 | 0.55 | 0.82 | 0.87 | 0.65 | 0.75 | 0.79 | 0.94 | ||
OA | 0.84 | |||||||||
k | 0.81 | |||||||||
9 | Parameter | Ntree | default (100) | |||||||
Mtry | 100 | |||||||||
Accuracy measures | precision | 0.94 | 0.43 | 0.78 | 0.90 | 0.69 | 0.78 | 0.76 | 0.89 | |
recall | 0.84 | 0.58 | 0.87 | 0.83 | 0.59 | 0.67 | 0.83 | 0.97 | ||
f1-score | 0.89 | 0.49 | 0.82 | 0.86 | 0.63 | 0.72 | 0.79 | 0.93 | ||
OA | 0.83 | |||||||||
k | 0.79 |
Experiment No. | Input Data | Class Name | Bare/Sparse Vegetation | Built-Up | Cropland | Forest | Herbaceous Wetland | Herbaceous Vegetation | Shrub | Water Bodies |
---|---|---|---|---|---|---|---|---|---|---|
Class ID | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||
No. of Pixels | 6424 | 575 | 4246 | 3101 | 602 | 3641 | 5283 | 3795 | ||
1 | Parameter | cost | default (1) | |||||||
gamma | Default (scale (1/(n_features * X.var()) as value of gamma, where X.var is variance) | |||||||||
Accuracy measures | precision | 0.91 | 0.79 | 0.64 | 0.76 | 0.96 | 0.67 | 0.72 | 0.95 | |
recall | 0.94 | 0.04 | 0.83 | 0.83 | 0.19 | 0.45 | 0.78 | 0.97 | ||
f1-score | 0.92 | 0.07 | 0.72 | 0.79 | 0.32 | 0.54 | 0.75 | 0.96 | ||
OA | 0.78 | |||||||||
k | 0.74 | |||||||||
2 | Parameter | cost | 50 | |||||||
gamma | default | |||||||||
Accuracy measures | precision | 0.94 | 0.78 | 0.78 | 0.87 | 0.80 | 0.76 | 0.75 | 0.97 | |
recall | 0.96 | 0.44 | 0.89 | 0.84 | 0.59 | 0.64 | 0.81 | 0.97 | ||
f1-score | 0.95 | 0.56 | 0.83 | 0.85 | 0.68 | 0.70 | 0.77 | 0.97 | ||
OA | 0.85 | |||||||||
k | 0.81 | |||||||||
3 | Parameter | cost | 100 | |||||||
gamma | default | |||||||||
Accuracy measures | precision | 0.94 | 0.78 | 0.78 | 0.87 | 0.74 | 0.76 | 0.75 | 0.97 | |
recall | 0.95 | 0.48 | 0.89 | 0.83 | 0.65 | 0.63 | 0.80 | 0.98 | ||
f1-score | 0.95 | 0.60 | 0.83 | 0.85 | 0.69 | 0.69 | 0.77 | 0.97 | ||
OA | 0.85 | |||||||||
k | 0.81 | |||||||||
4 | Parameter | cost | 300 | |||||||
gamma | default | |||||||||
Accuracy measures | precision | 0.95 | 0.73 | 0.78 | 0.86 | 0.72 | 0.78 | 0.74 | 0.96 | |
recall | 0.95 | 0.54 | 0.89 | 0.83 | 0.69 | 0.62 | 0.79 | 0.98 | ||
f1-score | 0.95 | 0.62 | 0.83 | 0.84 | 0.71 | 0.69 | 0.76 | 0.97 | ||
OA | 0.84 | |||||||||
k | 0.81 | |||||||||
5 | Parameter | cost | 600 | |||||||
gamma | default | |||||||||
Accuracy measures | precision | 0.95 | 0.67 | 0.77 | 0.85 | 0.74 | 0.78 | 0.74 | 0.96 | |
recall | 0.93 | 0.57 | 0.89 | 0.83 | 0.70 | 0.63 | 0.78 | 0.97 | ||
f1-score | 0.94 | 0.62 | 0.82 | 0.84 | 0.72 | 0.69 | 0.76 | 0.97 | ||
OA | 0.84 | |||||||||
k | 0.8 | |||||||||
6 | Parameter | cost | 900 | |||||||
gamma | default | |||||||||
Accuracy measures | precision | 0.94 | 0.67 | 0.76 | 0.85 | 0.75 | 0.78 | 0.74 | 0.96 | |
recall | 0.93 | 0.58 | 0.88 | 0.83 | 0.71 | 0.63 | 0.77 | 0.97 | ||
f1-score | 0.94 | 0.62 | 0.81 | 0.84 | 0.73 | 0.69 | 0.75 | 0.97 | ||
OA | 0.83 | |||||||||
k | 0.8 | |||||||||
7 | Parameter | cost | 1500 | |||||||
gamma | default | |||||||||
Accuracy measures | precision | 0.94 | 0.64 | 0.74 | 0.84 | 0.78 | 0.78 | 0.73 | 0.96 | |
recall | 0.91 | 0.61 | 0.88 | 0.83 | 0.70 | 0.62 | 0.77 | 0.97 | ||
f1-score | 0.93 | 0.62 | 0.80 | 0.83 | 0.74 | 0.69 | 0.75 | 0.97 | ||
OA | 0.83 | |||||||||
k | 0.8 | |||||||||
8 | Parameter | cost | 300 | |||||||
gamma | 10−3 | |||||||||
Accuracy measures | precision | 0.24 | 1.00 | 1.00 | 0.00 | 0.00 | 0.50 | 1.00 | 1.00 | |
recall | 1.00 | 0.02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.15 | ||
f1-score | 0.38 | 0.04 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.26 | ||
OA | 0.25 | |||||||||
k | 0.03 | |||||||||
9 | Parameter | cost | 300 | |||||||
gamma | 10−6 | |||||||||
Accuracy measures | precision | 0.99 | 1.00 | 1.00 | 0.00 | 0.00 | 0.50 | 0.20 | 1.00 | |
recall | 0.05 | 0.02 | 0.01 | 0.00 | 0.00 | 0.00 | 1.00 | 0.15 | ||
f1-score | 0.10 | 0.04 | 0.02 | 0.00 | 0.00 | 0.00 | 0.33 | 0.26 | ||
OA | 0.22 | |||||||||
k | 0.04 | |||||||||
10 | Parameter | cost | 300 | |||||||
gamma | 10−8 | |||||||||
Accuracy measures | precision | 0.94 | 0.61 | 0.78 | 0.88 | 0.77 | 0.80 | 0.78 | 0.80 | |
recall | 0.85 | 0.71 | 0.83 | 0.82 | 0.71 | 0.72 | 0.78 | 0.99 | ||
f1-score | 0.89 | 0.65 | 0.80 | 0.84 | 0.74 | 0.75 | 0.78 | 0.88 | ||
Overall accuracy | 0.82 | |||||||||
Kappa coefficient | 0.79 | |||||||||
11 | Parameter | cost | 300 | |||||||
gamma | 10−9 | |||||||||
Accuracy measures | precision | 0.95 | 0.73 | 0.78 | 0.86 | 0.72 | 0.78 | 0.74 | 0.96 | |
recall | 0.95 | 0.54 | 0.89 | 0.83 | 0.69 | 0.62 | 0.79 | 0.98 | ||
f1-score | 0.95 | 0.62 | 0.83 | 0.84 | 0.71 | 0.69 | 0.76 | 0.97 | ||
OA | 0.84 | |||||||||
k | 0.81 | |||||||||
12 | Parameter | cost | 300 | |||||||
gamma | 10−12 | |||||||||
Accuracy measures | precision | 0.92 | 0.85 | 0.66 | 0.78 | 0.92 | 0.67 | 0.74 | 0.96 | |
recall | 0.96 | 0.15 | 0.84 | 0.84 | 0.24 | 0.47 | 0.77 | 0.97 | ||
f1-score | 0.94 | 0.26 | 0.74 | 0.81 | 0.39 | 0.55 | 0.76 | 0.97 | ||
OA | 0.80 | |||||||||
k | 0.76 |
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Share and Cite
Adugna, T.; Xu, W.; Fan, J. Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote Sens. 2022, 14, 574. https://doi.org/10.3390/rs14030574
Adugna T, Xu W, Fan J. Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote Sensing. 2022; 14(3):574. https://doi.org/10.3390/rs14030574
Chicago/Turabian StyleAdugna, Tesfaye, Wenbo Xu, and Jinlong Fan. 2022. "Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images" Remote Sensing 14, no. 3: 574. https://doi.org/10.3390/rs14030574