A Novel Hierarchical Clustering Sequential Forward Feature Selection Method for Paddy Rice Agriculture Mapping Based on Time-Series Images

Xingyin Duan, Xiaobo Wu (), Jie Ge, Li Deng, Liang Shen, Jingwen Xu, Xiaoying Xu, Qin He, Yixin Chen, Xuesong Gao and Bing Li
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Xingyin Duan: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Xiaobo Wu: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Jie Ge: Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China
Li Deng: Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China
Liang Shen: Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China
Jingwen Xu: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Xiaoying Xu: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Qin He: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Yixin Chen: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Xuesong Gao: College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Bing Li: College of Resources, Sichuan Agricultural University, Chengdu 611130, China

Agriculture, 2024, vol. 14, issue 9, 1-20

Abstract: Timely and accurate mapping of rice distribution is crucial to estimate yield, optimize agriculture spatial patterns, and ensure global food security. Feature selection (FS) methods have significantly improved computational efficiency by reducing redundancy in spectral and temporal feature sets, playing a vital role in identifying and mapping paddy rice. However, the optimal feature sets selected by existing methods suffer from issues such as information redundancy or local optimality, limiting their accuracy in rice identification. Moreover, the effects of these FS methods on rice recognition in various machine learning classifiers and regions with different climatic conditions and planting structures is still unclear. To overcome these limitations, we conducted a comprehensive evaluation of the potential applications of major FS methods, including the wrapper method, embedded method, and filter method for rice mapping. A novel hierarchical lustering sequential forward selection (HCSFS) method for precisely extracting the optimal feature set for rice identification is proposed. The accuracy of the HCSFS and other FS methods for rice identification was tested with nine common machine learning classifiers. The results indicated that, among the three FS methods, the wrapper method achieved the best rice mapping performance, followed by the embedded method, and lastly, the filter method. The new HCSFS significantly reduced redundant features compared with eleven typical FS methods, demonstrating higher precision and stability, with user accuracy and producer accuracy exceeding 0.9548 and 0.9487, respectively. Additionally, the spatial distribution of rice maps generated using the optimal feature set selected by HCSFS closely aligned with actual planting patterns, markedly outperforming existing rice products. This research confirms the effectiveness and transferability of the HCSFS method for rice mapping across different climates and cultivation structures, suggesting its enormous potential for classifying other crops using time-series remote sensing images.

Keywords: Sentinel-2; time-series spectral feature; HCSFS; feature selection; rice mapping (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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