A Novel Hierarchical Clustering Sequential Forward Feature Selection Method for Paddy Rice Agriculture Mapping Based on Time-Series Images
Abstract
:1. Introduction
- (1)
- The potential of key FS methods in rice mapping is comprehensively evaluated;
- (2)
- A robust HCSFS method is proposed to address the issues of feature redundancy and local optimization in existing FS methods;
- (3)
- The new method demonstrates effective transferability across different spatial contexts (regions with different agricultural planting structures and climates) and temporal contexts (early identification), thereby offering advanced technical support for precision agriculture.
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Sentinel-2 Imagery
2.2.2. Validation Data
2.3. Overview of Methods
2.4. Construction of Time-Series Spectral Feature Sets
2.5. Feature Selection
2.5.1. HCSFS
2.5.2. Other Feature Selection Methods
2.6. Classification and Evaluation
3. Results
3.1. Comparison of HCSFS with Other Methods
3.1.1. The Number of the Optimal Feature Sets
3.1.2. Accuracy Stability of Feature Selection Method
3.2. Separability of the Optimal Feature Sets
3.3. Quality Assessment of Paddy Rice Maps
3.3.1. Paddy Rice Probability Maps—HCSFS
3.3.2. Comparison of Rice Spatial Distribution Map of in Different Methods
4. Discussion
4.1. Transferability of the HCSFS
4.2. The Applicability of the HCSFS in Early-Season Rice Identification
4.3. Limitations of the HCSFS
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Types | Rice | Corn | Soybean | Other Crops | Garden | Forest | Grass | Water | Building | Bare Soil | Total | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Site A | Train | 135 | 20 | 7 | 29 | 17 | 14 | 10 | 20 | 11 | 7 | 270 |
Valid | 312 | 80 | 13 | 64 | 33 | 36 | 30 | 30 | 19 | 23 | 640 | |
Site B | Train | 135 | 24 | 24 | 6 | 15 | 21 | 6 | 15 | 15 | 9 | 270 |
Valid | 315 | 56 | 56 | 14 | 35 | 49 | 14 | 35 | 35 | 21 | 630 |
Index Name | Formula | Reference |
---|---|---|
NDVI | [37] | |
EVI | [38] | |
LSWI | [7] | |
NDSVI | [10] | |
NDTI | [8] | |
RENDVI | [9] | |
REP | [9] | |
PSRI | [33] |
FS Model | The Optimal Feature Set | |
---|---|---|
Site A | HCSFS | ‘LSWI_07_15’, ‘PSRI_09_28’, ‘PSRI_05_16’, ‘NDTI_09_28’, ‘NDSVI_09_28’, ‘SWIR1_08_14’, ‘RE2_05_31’, ‘SWIR2_08_14’ |
SFS | ‘LSWI_07_15’, ‘PSRI_09_28’, ‘PSRI_05_16’, ‘NDTI_09_28’, ‘NDSVI_09_13’, ‘EVI_08_14’, ‘REP_08_29’, ‘RENDVI_09_28’ | |
Site B | HCSFS | ‘RENDVI_08_15’, ‘RENDVI_07_01’, ‘RENDVI_06_16’, ‘RENDVI_09_29’, ‘EVI_07_31’, ‘PSRI_09_14’ |
SFS | ‘RENDVI_08_15’, ‘RENDVI_07_01’, ‘REP_07_31’ |
Wrapper Methods | Filter Methods | Embedded Methods | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
FS Method | HCSFS | SFS | SBS | RFE | JM | Relief | mRMR | PSTFS | XGBoost | DT | RF | PI |
Site A | 83.44 | 115.60 | 37.35 | 18.17 | 1.04 | 8.80 | 10.99 | 21.56 | 17.39 | 17.71 | 17.54 | 23.73 |
Site B | 97.54 | 51.66 | 34.75 | 16.96 | 0.89 | 9.03 | 11.40 | 8.60 | 17.12 | 16.65 | 17.23 | 23.37 |
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Duan, X.; Wu, X.; Ge, J.; Deng, L.; Shen, L.; Xu, J.; Xu, X.; He, Q.; Chen, Y.; Gao, X.; et al. A Novel Hierarchical Clustering Sequential Forward Feature Selection Method for Paddy Rice Agriculture Mapping Based on Time-Series Images. Agriculture 2024, 14, 1468. https://doi.org/10.3390/agriculture14091468
Duan X, Wu X, Ge J, Deng L, Shen L, Xu J, Xu X, He Q, Chen Y, Gao X, et al. A Novel Hierarchical Clustering Sequential Forward Feature Selection Method for Paddy Rice Agriculture Mapping Based on Time-Series Images. Agriculture. 2024; 14(9):1468. https://doi.org/10.3390/agriculture14091468
Chicago/Turabian StyleDuan, Xingyin, Xiaobo Wu, Jie Ge, Li Deng, Liang Shen, Jingwen Xu, Xiaoying Xu, Qin He, Yixin Chen, Xuesong Gao, and et al. 2024. "A Novel Hierarchical Clustering Sequential Forward Feature Selection Method for Paddy Rice Agriculture Mapping Based on Time-Series Images" Agriculture 14, no. 9: 1468. https://doi.org/10.3390/agriculture14091468