• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 3, 947 (2022)
Feng-hua YU*, Dan ZHAO1;, Zhong-hui GUO1;, Zhong-yu JIN1;, Shuang GUO1;, Chun-ling CHEN1; 2; *;, and Tong-yu XU1; 2;
Author Affiliations
  • 1. College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
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    DOI: 10.3964/j.issn.1000-0593(2022)03-0947-07 Cite this Article
    Feng-hua YU, Dan ZHAO, Zhong-hui GUO, Zhong-yu JIN, Shuang GUO, Chun-ling CHEN, Tong-yu XU. Characteristic Analysis and Decomposition of Mixed Pixels From UAV Hyperspectral Images in Rice Tillering Stage[J]. Spectroscopy and Spectral Analysis, 2022, 42(3): 947 Copy Citation Text show less
    Location of the study area and the non-homogeneous state of the canopy of rice at tillering stage
    Fig. 1. Location of the study area and the non-homogeneous state of the canopy of rice at tillering stage
    Registration results of visible light image (b) and hyperspectral remote sensing image (a)
    Fig. 2. Registration results of visible light image (b) and hyperspectral remote sensing image (a)
    Hyperspectral reflectance spectra of rice pixels, water layer pixels and mixed pixels
    Fig. 3. Hyperspectral reflectance spectra of rice pixels, water layer pixels and mixed pixels
    Reflectance spectra of rice under different abundance conditions
    Fig. 4. Reflectance spectra of rice under different abundance conditions
    Object-oriented(a) and supervise (b) classification results
    Fig. 5. Object-oriented
    (a) and supervise (b) classification results
    Hyperspectral reflectance spectra of 24 pure water bodies in rice fields
    Fig. 6. Hyperspectral reflectance spectra of 24 pure water bodies in rice fields
    Results of supervised classification
    Fig. 7. Results of supervised classification
    Unmixing results of mixed pixels under different endmember abundance conditions
    Fig. 8. Unmixing results of mixed pixels under different endmember abundance conditions
    总体分
    类精度
    /%
    Kappa
    系数
    错分
    误差
    /%
    漏分
    误差
    /%
    制图
    精度
    /%
    用户
    精度
    /%
    监督分类水体
    水稻
    99.458 60.986 30.42
    0.87
    0.32
    1.13
    99.67
    98.87
    99.58
    94.52
    面向对象水体
    水稻
    98.213 80.954 11.99
    1.17
    0.42
    5.47
    99.58
    99.12
    98.00
    98.80
    Table 1. Classification results of different methods
    水稻丰度可见光分类丰度估计完全约束最小二乘法
    pic10.906 90.806 7
    pic20.467 10.945 2
    pic30.439 40.620 9
    pic40.104 00.479 3
    pic50.847 50.815 9
    pic60.157 90.842 3
    Table 2. Results of rice abundance estimation by different methods
    Feng-hua YU, Dan ZHAO, Zhong-hui GUO, Zhong-yu JIN, Shuang GUO, Chun-ling CHEN, Tong-yu XU. Characteristic Analysis and Decomposition of Mixed Pixels From UAV Hyperspectral Images in Rice Tillering Stage[J]. Spectroscopy and Spectral Analysis, 2022, 42(3): 947
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