Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.2. Methods
2.2.1. Optimized Image Segmentation
2.2.2. Feature Extraction and Selection
2.2.3. Terrace Mapping Using ML Classifiers
2.2.4. Mapping Accuracy Assessment
3. Results and Analysis
3.1. Image Segmentation and Feature Selection Results
3.2. Terrace Mapping Results and Accuracy Assessment
4. Discussion
4.1. Influence of Class Imbalance and Feature Selection on Terrace Mapping
4.2. General Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Type | Features | Descriptions |
---|---|---|
Spectrum | Mean band (Red, Green, Blue, and Panchromatic band) | The mean intensity of all pixels forming an image object within each band, |
Brightness | The mean value of the C of all layers, | |
Maximum Difference | Spectrum difference of all layers, | |
Geometry | Shape index | The smoothness of an image object border, |
Length–width | A length-to-width ratio of an image object, | |
Roundness | Similarity of an object to an ellipse, | |
Area | The number of pixels forming an image object. | |
Topography | Mean value (Elevation, Curvature, Roughness, Slope, and Shaded relief) | The mean intensity of all pixels forming an image object within each topographic layer. |
Terrain texture | Gray-level co-occurrence matrix (GLCM) homogeneity | The GLCM measures how often different combinations of pixel gray levels occur in a scene. In this study, the terrain texture features were derived from GLCM based on five topographic layers. The detail for calculating GLCM was taken from the study by Haralick et al. (1973) [63]. |
GLCM contrast | ||
GLCM dissimilarity | ||
GLCM entropy | ||
GLCM angular second moment | ||
GLCM mean | ||
GLCM standard deviation | ||
GLCM correlation |
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Type | Features |
---|---|
Spectrum | Mean band (Red, Green, Blue, and Panchromatic band), Maximum Difference, and Brightness |
Geometry | Shape index, Length–width, Roundness, and Area |
Topography | Mean value (Elevation, Curvature, Roughness, Slope, and Shaded relief) |
Terrain texture | Gray-level co-occurrence matrix (GLCM) homogeneity, GLCM contrast, GLCM dissimilarity, GLCM entropy, GLCM angular second moment, GLCM mean, GLCM standard deviation, and GLCM correlation based on Elevation, Curvature, Roughness, Slope, and Shaded relief |
Feature Type | Feature | Iteration | Average Rank |
---|---|---|---|
Spectrum | Mean value of panchromatic band | 20 | 1.0 |
Terrain texture | GLCM angular second moment of curvature | 20 | 2.4 |
Terrain texture | GLCM homogeneity of shade relief | 20 | 5.0 |
Terrain texture | GLCM homogeneity of curvature | 20 | 5.9 |
Spectrum | Maximum difference of value | 20 | 7.0 |
Spectrum | Mean value of band red | 20 | 7.1 |
Terrain texture | GLCM mean value of shade relief | 20 | 7.1 |
Geometry | Length/Width | 20 | 7.3 |
Terrain texture | GLCM angular second moment of elevation | 20 | 9.8 |
Terrain texture | GLCM homogeneity of slope | 20 | 10.4 |
Topography | Mean value of shade relief | 20 | 10.9 |
Terrain texture | GLCM angular second moment of shade relief | 20 | 12.0 |
Terrain texture | GLCM mean value of roughness | 20 | 12.4 |
Terrain texture | GLCM homogeneity of roughness | 19 | 13.0 |
Terrain texture | GLCM standard derivation of curvature | 18 | 14.1 |
Spectrum | Mean value of band green | 16 | 19.0 |
Terrain texture | GLCM mean value of slope | 11 | 19.7 |
Geometry | Area | 12 | 20.5 |
Topography | Mean value of elevation | 8 | 20.9 |
Geometry | Shape index | 5 | 22.1 |
Classifier | Mapping Results | Reference (m2) | Pr | Re | F1 | OA | Kappa | |
---|---|---|---|---|---|---|---|---|
Terraces | Non-Terraces | |||||||
XGBoost | Terraces | 222,855.55 | 49,058.78 | 81.96% | 81.48% | 81.72% | 94.19% | 0.78 |
Non-Terraces | 50,638.18 | 1,393,215.99 | ||||||
RF | Terraces | 228,722.81 | 30,747.19 | 88.15% | 83.63% | 85.83% | 95.60% | 0.83 |
Non-Terraces | 44,770.92 | 1,411,527.58 | ||||||
KNN | Terraces | 222,743.59 | 80,880.65 | 73.36% | 81.44% | 77.19% | 92.33% | 0.73 |
Non-Terraces | 50,750.14 | 1,361,394.12 |
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Ding, H.; Na, J.; Jiang, S.; Zhu, J.; Liu, K.; Fu, Y.; Li, F. Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China. Remote Sens. 2021, 13, 1021. https://doi.org/10.3390/rs13051021
Ding H, Na J, Jiang S, Zhu J, Liu K, Fu Y, Li F. Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China. Remote Sensing. 2021; 13(5):1021. https://doi.org/10.3390/rs13051021
Chicago/Turabian StyleDing, Hu, Jiaming Na, Shangjing Jiang, Jie Zhu, Kai Liu, Yingchun Fu, and Fayuan Li. 2021. "Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China" Remote Sensing 13, no. 5: 1021. https://doi.org/10.3390/rs13051021