Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification
Abstract
:1. Introduction
2. Methodology
2.1. Study Site and Data Preparation
2.2. Segmentation-Based Analysis
- Image 1: Bands 2, 3 and 5;
- Image 2: Bands 2, 3 and 7;
- Image 3: Bands 1, 5 and 8;
- Image 4: Bands 2, 6 and 8;
Correlation | Band 1 | Band 2 | Band 3 | Band 4 | Band 5 | Band 6 | Band 7 | Band 8 |
---|---|---|---|---|---|---|---|---|
Band 1 | 1 | |||||||
Band 2 | 0.990286 | 1 | ||||||
Band 3 | 0.974494 | 0.987039 | 1 | |||||
Band 4 | 0.937438 | 0.942091 | 0.965501 | 1 | ||||
Band 5 | 0.915844 | 0.929299 | 0.955034 | 0.984436 | 1 | |||
Band 6 | 0.766527 | 0.779873 | 0.846188 | 0.885299 | 0.865134 | 1 | ||
Band 7 | 0.518168 | 0.538644 | 0.620546 | 0.621782 | 0.608105 | 0.897162 | 1 | |
Band 8 | 0.498322 | 0.512075 | 0.591015 | 0.606024 | 0.587367 | 0.877953 | 0.981882 | 1 |
GA Procedure Parameters | Search Space | ||||
---|---|---|---|---|---|
Min. | Max. | ||||
No of experiments | 10 | Scale parameter | 0 | 40 | |
Population size | 20 | Weight color | 0 | 1 | |
No of generations | 50 | Weight compactness | 0 | 1 |
2.3. Feature Selection
- Group 1 (G1): Grass (311) and Trees (150);
- Group 2 (G2): Ceramic Tile Roofs (151) and Bare Soil (141);
- Group 3 (G3): Concrete (196) and Clear Asbestos Roofs (86);
- Group 4 (G4): Asphalt (53) and Dark Asbestos Roofs (52).
2.4. Land Cover Classification Analysis
3. Results
3.1. Segmentation Parameters-Based Analysis
3.2. Feature Selection Analysis
Group 1 | InfoGain | Relief-F | FCBF | RF | ||||
Ra | RVb | Ra | RVb | Ra | RVb | Ra | RVb | |
Brightness | 4 | 2.14 | ||||||
Brightness – Quickc | 4 | 0.411 | ||||||
GLDV Ang. 2nd moment Layer 1 (135°) | 3 | 0.473 | ||||||
GLDV Contrast Layer 4 (135°) | 5 | 0.211 | ||||||
Max.pixelvalueLayer3 | 5 | 2.14 | ||||||
Mean Layer 3 | 1 | 0.824 | 1 | 0.309 | 1 | 0.906 | 2 | 2.83 |
Mean Layer 4 | 4 | 0.728 | 3 | 0.224 | ||||
Min. pixel value Layer 3 | 2 | 0.787 | 2 | 0.272 | 1 | 2.86 | ||
Min. pixel value Layer 4 | 3 | 0.728 | 4 | 0.221 | 2 | 0.706 | ||
Min. pixel value Layer 5 | 5 | 0.661 | ||||||
Min.pixelvalueLayer2 | 3 | 2.55 | ||||||
Ratio2 – Quickc | 5 | 0.198 | ||||||
Group 2 | InfoGain | Relief-F | FCBF | RF | ||||
Ra | RVb | Ra | RVb | Ra | RVb | Ra | RVb | |
GLDV Ang. 2nd moment (0°) | 4 | 0.359 | 3 | 0.282 | ||||
GLDV Ang. 2nd moment Layer 8 (all dir.) | 3 | 0.88 | ||||||
GLDV Entropy (0°) | 4 | 0.73 | ||||||
GLDV Entropy Layer 7 (90°) | 1 | 0.317 | ||||||
Mean Layer 3 | 4 | 0.102 | 2 | 1.01 | ||||
Min. pixel value Layer 1 | 5 | 0.354 | 5 | 0.71 | ||||
Min. pixel value Layer 2 | 1 | 0.401 | 2 | 0.104 | 4 | 0.272 | ||
Min. pixel value Layer 3 | 3 | 0.379 | 3 | 0.103 | 1 | 1.24 | ||
Ratio Layer 3 | 5 | 0.228 | ||||||
Standard deviation Layer 6 | 5 | 0.099 | ||||||
Standard deviation Layer 7 | 2 | 0.384 | 1 | 0.106 | 2 | 0.302 | ||
Group 3 | InfoGain | Relief-F | FCBF | RF | ||||
Ra | RVb | Ra | RVb | Ra | RVb | Ra | RVb | |
GLCM Correlation Layer 7 (135°) | 4 | 0.118 | ||||||
GLCM StdDev Layer 6 (135°) | 3 | 0.128 | ||||||
GLDV Entropy Layer 4 (45°) | 5 | 0.082 | ||||||
Mean Layer 1 | 2 | 0.159 | ||||||
Ratio Layer 1 – Quickc | 5 | 0.124 | ||||||
Ratio Layer 2 | 2 | 0.132 | 3 | 1.29 | ||||
Ratio Layer 2 – Quickc | 2 | 0.302 | ||||||
Ratio Layer 3 | 1 | 0.311 | 1 | 0.413 | 2 | 1.32 | ||
Ratio Layer 4 – Quickc | 3 | 0.293 | 4 | 0.125 | ||||
Ratio Layer 6 | 1 | 1.76 | ||||||
Ratio Layer 7 | 5 | 0.293 | 3 | 0.126 | 4 | 1.27 | ||
Ratio Layer 8 | 4 | 0.293 | 1 | 0.136 | 5 | 1.25 | ||
Group 4 | InfoGain | Relief-F | FCBF | RF | ||||
Ra | RVb | Ra | RVb | Ra | RVb | Ra | RVb | |
GLCM Dissimilarity (90°) | 4 | 0.223 | ||||||
GLCM Dissimilarity Layer 8 (0°) | 5 | 0.155 | ||||||
Max. Diff. | 4 | 0.385 | 2 | 0.223 | 5 | 0.29 | ||
Max. pixel value Layer 8 | 3 | 0.289 | ||||||
Ratio Layer 2 – Quickc | 2 | 0.514 | 3 | 0.181 | 1 | 0.48 | ||
Ratio Layer 3 | 1 | 0.536 | 2 | 0.209 | 1 | 0.155 | ||
Ratio Layer 4 | 5 | 0.132 | 4 | 0.31 | ||||
Ratio Layer 4 – Quickc | 5 | 0.358 | 4 | 0.137 | 3 | 0.35 | ||
Ratio Layer 8 | 3 | 0.503 | 1 | 0.269 | 2 | 0.47 |
3.3. Classification-Based Analysis
4. Conclusions and Suggestions
Acknowledgments
References
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Novack, T.; Esch, T.; Kux, H.; Stilla, U. Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification . Remote Sens. 2011, 3, 2263-2282. https://doi.org/10.3390/rs3102263
Novack T, Esch T, Kux H, Stilla U. Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification . Remote Sensing. 2011; 3(10):2263-2282. https://doi.org/10.3390/rs3102263
Chicago/Turabian StyleNovack, Tessio, Thomas Esch, Hermann Kux, and Uwe Stilla. 2011. "Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification " Remote Sensing 3, no. 10: 2263-2282. https://doi.org/10.3390/rs3102263
APA StyleNovack, T., Esch, T., Kux, H., & Stilla, U. (2011). Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification . Remote Sensing, 3(10), 2263-2282. https://doi.org/10.3390/rs3102263