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

Author

Listed:
  • Xingyin Duan

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

  • Xiaobo Wu

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China
    Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, 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
    Sichuan Institute of Land Science and Technology (Sichuan Center of Satellite Application Technology), Chengdu 610045, China)

  • Li Deng

    (Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China
    Sichuan Institute of Land Science and Technology (Sichuan Center of Satellite Application Technology), Chengdu 610045, China)

  • Liang Shen

    (Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China
    Surveying and Mapping Geographic Information Center, Sichuan Institute of Geological Survey, Chengdu 610072, China)

  • Jingwen Xu

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China
    Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, 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
    Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China)

  • Bing Li

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China
    Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China)

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.

Suggested Citation

  • Xingyin Duan & Xiaobo Wu & Jie Ge & Li Deng & Liang Shen & Jingwen Xu & Xiaoying Xu & Qin He & Yixin Chen & Xuesong Gao & Bing Li, 2024. "A Novel Hierarchical Clustering Sequential Forward Feature Selection Method for Paddy Rice Agriculture Mapping Based on Time-Series Images," Agriculture, MDPI, vol. 14(9), pages 1-20, August.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:9:p:1468-:d:1465661
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