Spectroscopy and Spectral Analysis, Volume. 42, Issue 6, 1948(2022)

Intelligent Recognition of Corn Residue Cover Area by Time-Series Sentinel-2A Images

Wan-cheng TAO*, Ying ZHANG1; 2;, Zi-xuan XIE1; 2;, Xin-sheng WANG1; 2;, Yi DONG1; 2;, Ming-zheng ZHANG1; 2;, Wei SU1; 2; *;, Jia-yu LI1; 2;, and Fu XUAN1; 2;
Author Affiliations
  • 1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China
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    Figures & Tables(14)
    Study area and Sentinel-2A acquired on November 4, 2020 (a), photos of cornresidue cover areas (b)
    Time-Sequence NDVI and NDRI change curves of corn planting areas in the study area in 2020(a): Temporal NDVI variation curve; (b): NDRI variation curve during H-S2
    Flowchart of identification technology of corn residue cover area
    Spectral reflectance characteristics of different ground types (Sentinel-2A images on 4 November 2020)(a): Corn stalk residue; (b): Rice stalk residue; (c): Water; (d): Woods
    Spectral reflectance characteristics of different crops (Sentinel-2A image on 22 July 2020)(a): Corn; (b): Rice
    QT feature construction schematic diagram(a): Blue wave heat map; (b): Single pixel time series data; (c): Time series data selection; (d): Quantile dataset
    Connected domain calibration schematic diagram(a): Numerical diagram of calssification area;(b): Schematic diagram of connected domain of pixels
    Visualization results of classification based on different feature dataset(a): The original sub-image 1_w; (b): The result of M1; (c): The result of M2; (d): The result of M3; (e): The result of M4; (f): The result of M5
    Combined with the connected domain calibration classification result 1(a): The original sub-image 1_S; (b): The result of M5_1; (c): The result of M5; (d): The result of M6
    Combined with the classification result of connected domain calibration 2(a): The original sub-image 2_S; (b): The original sub-image 2_W; (c): The result of M5_1; (d): The result of M5; (e): The result of M6
    Identification results of corn residuecover area(a): The sub-image 1 of calssification result;(b): The sub-image 2 of calssification result
    • Table 1. Quantitative evaluation of classification models for different decision trees

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      Table 1. Quantitative evaluation of classification models for different decision trees

      决策树数量Kappa/%OA总体分类精度/%
      593.9795.07
      1094.5495.61
      2096.6595.76
      3097.4197.91
      4096.9897.54
    • Table 2. Classification quantitative evaluation results based on different feature dataset

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      Table 2. Classification quantitative evaluation results based on different feature dataset

      模型光谱特征集指数特征集QT特征集Kappa/%OA/%
      M1+--91.5793.22
      M2++-92.8994.27
      M3-++94.1795.31
      M4+-+96.1296.87
      M5+++97.4197.91
    • Table 3. Quantitative evaluation results based on different time scale feature classification

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      Table 3. Quantitative evaluation results based on different time scale feature classification

      5月—6月7月8月9月10月11月Kappa/%OA/%
      M5_1-----+93.5194.79
      M5_2----++92.5494.02
      M5_3---+++92.1593.71
      M5_4--++++93.8995.17
      M5_5-+++++95.4696.35
      M5_6++++++97.4197.91
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    Wan-cheng TAO, Ying ZHANG, Zi-xuan XIE, Xin-sheng WANG, Yi DONG, Ming-zheng ZHANG, Wei SU, Jia-yu LI, Fu XUAN. Intelligent Recognition of Corn Residue Cover Area by Time-Series Sentinel-2A Images[J]. Spectroscopy and Spectral Analysis, 2022, 42(6): 1948

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    Paper Information

    Category: Research Articles

    Received: Jun. 1, 2021

    Accepted: --

    Published Online: Nov. 14, 2022

    The Author Email: Wan-cheng TAO (B20203210937@cau.edu.cn)

    DOI:10.3964/j.issn.1000-0593(2022)06-1948-08

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