A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Sources
- City represented by the video satellite remote sensing data: New Delhi, India;
- Size of the data: 600 × 950 pixels;
- Ground resolution of the data: approximately 1.13 m;
- Bandwidths of the data: blue band, 437–512 nm; green band, 489–585 nm; and red band: 580–723 nm;
- Dynamic range of the data: 8 bits (0–255) for each channel;
- Data acquisition duration: 28 s; number of observation angles (frames): 700.
2.2. Experimental Methods
3. Results
3.1. Classification Results Obtained Using Single-Angle Data
3.2. Confusion Matrix Comparative Analysis between Single-Angle Images and Multiangle Images
3.3. Results Derived Using the SVM Classification Method
3.4. Results Derived Using the Ensemble Learning Classification Method
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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No. | Class Name | Number of Samples |
---|---|---|
1 | Grasslands | 924 |
2 | Trees | 6715 |
3 | Bare soils | 5561 |
4 | Building (factory district) | 3322 |
5 | Building (residential district) | 2634 |
6 | Building (business district) | 3891 |
7 | Asphalt roads | 2915 |
8 | Concrete roads | 335 |
Total | \ | 26,315 |
Training Set Radio | 1% | 2.5% | 5% | 10% | 20% | 40% | 80% | |
---|---|---|---|---|---|---|---|---|
Number of Angles | ||||||||
1 | 56.9 | 60.0 | 61.7 | 63.6 | 65.1 | 64.2 | 66.2 | |
2 | 60.6 | 61.0 | 63.4 | 64.5 | 65.6 | 64.9 | 67.3 | |
4 | 62.4 | 63.1 | 69.1 | 69.5 | 69.5 | 69.7 | 72.8 | |
8 | 65.7 | 69.7 | 72.1 | 73.0 | 75.9 | 76.5 | 77.0 | |
16 | 69.3 | 70.8 | 73.7 | 74.8 | 77.2 | 77.8 | 78.6 | |
32 | 70.3 | 73.1 | 76.8 | 78.1 | 79.4 | 80.2 | 80.9 | |
64 | 70.7 | 74.5 | 78.5 | 79.9 | 81.0 | 82.0 | 83.1 | |
128 | 71.3 | 76.9 | 79.7 | 81.3 | 82.6 | 83.7 | 84.9 | |
256 | 71.5 | 77.0 | 80.5 | 82.1 | 83.7 | 84.8 | 85.9 | |
512 | 71.6 | 77.7 | 80.7 | 82.4 | 84.2 | 85.4 | 85.5 | |
700 | 75.1 | 78.0 | 81.0 | 82.7 | 85.1 | 85.6 | 88.0 |
Training Set Ratio | 1% | 2.5% | 5% | 10% | 20% | 40% | 80% | |
---|---|---|---|---|---|---|---|---|
Number of Angles | ||||||||
1 | 51.5 | 52.7 | 54.5 | 54.5 | 55.6 | 56.2 | 60.4 | |
2 | 55.0 | 56.4 | 56.6 | 56.6 | 57.0 | 57.9 | 62.9 | |
4 | 61.8 | 64.2 | 63.8 | 64.5 | 64.1 | 66.6 | 67.7 | |
8 | 64.6 | 66.5 | 66.9 | 66.8 | 67.0 | 68.9 | 69.3 | |
16 | 65.9 | 70.3 | 72.4 | 71.8 | 72.4 | 72.3 | 73.5 | |
32 | 67.3 | 73.1 | 74.3 | 74.7 | 75.9 | 76.0 | 76.4 | |
64 | 68.4 | 74.6 | 76.6 | 77.4 | 77.9 | 78.0 | 78.0 | |
128 | 68.7 | 76.9 | 78.0 | 79.1 | 79.7 | 80.0 | 80. 8 | |
256 | 68.5 | 79.0 | 80.0 | 80.2 | 80.6 | 81.0 | 81.1 | |
512 | 68.5 | 79.6 | 80.7 | 81.1 | 81.6 | 82.2 | 82.4 | |
700 | 72.0 | 81.0 | 80.8 | 81.4 | 81.9 | 82.7 | 83.0 |
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Ye, F.; Ai, T.; Wang, J.; Yao, Y.; Zhou, Z. A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data. Remote Sens. 2022, 14, 2324. https://doi.org/10.3390/rs14102324
Ye F, Ai T, Wang J, Yao Y, Zhou Z. A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data. Remote Sensing. 2022; 14(10):2324. https://doi.org/10.3390/rs14102324
Chicago/Turabian StyleYe, Fanghong, Tinghua Ai, Jiaming Wang, Yuan Yao, and Zheng Zhou. 2022. "A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data" Remote Sensing 14, no. 10: 2324. https://doi.org/10.3390/rs14102324
APA StyleYe, F., Ai, T., Wang, J., Yao, Y., & Zhou, Z. (2022). A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data. Remote Sensing, 14(10), 2324. https://doi.org/10.3390/rs14102324