Winter Wheat Planting Information Extraction Based on Principal Component and Cloud Model
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摘要:
在对Sentinel-2卫星遥感影像进行预处理的基础上，利用主成分变化提取小麦主要信息，基于云模型算法开展光谱遥感图像分类。分类时，首先根据训练样本集，由逆向云发生器生成典型小麦的云模型，然后利用云发生器计算出各波段每个象元对小麦地物的平均隶属度，在对各波段的隶属度分析基础上，摒弃含有复杂信息的第1主成分，利用第2主成分和第3主成分信息实现对冬小麦种植空间信息的提取。结果表明，提取小麦种植信息制图精度和用户精度分别为92.78%和99.90%，小麦种植田块的隶属度值因小麦长势和密度的不同有较大的差异，云模型对长势较差、密度较低的小麦像元存在漏分现象。基于云模型的算法精度极高，对小麦地块的识别错分、漏分现象少。该模型有助于冬小麦种植面积的精确提取，对于农业部门进行冬小麦生长监测与产量估测有重要的支撑作用。
Abstract:Based on preprocessing of Sentinel-2 satellite remote sensing images, main information of wheat was extracted by principal component changes, and then a spectral remote sensing image classification method based on cloud model was applied．When classifying, firstly, according to training sample set, cloud model of typical wheat was generated by reverse cloud generator, and then cloud generator was used to calculate average membership degree of each pixel in each band to wheat ground features．On basis of analysis, the first principal component containing complex information was discarded, and the second and third principal component information was used to extract spatial information of winter wheat planting．Results showed that mapping accuracy and user accuracy of extracting wheat planting information were 92.78% and 99.90%, respectively．Membership value of wheat planting fields had great differences due to difference of wheat growth and density．Cloud model of poor growth and density, which resulted in low wheat pixels, had phenomenon of missing points．In general, algorithm based on cloud model was highly accurate, and there were few misclassifications and omissions in identification of wheat field．Algorithm would play an important supporting role in agricultural sector for winter wheat growth monitoring and yield estimation．
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Keywords:
- principal component /
- cloud model /
- wheat /
- planting information
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表 1 哨兵-2A 光谱波段信息
Table 1. Sentinel-2A spectral band information
波段 波长/μm 分辨率/m Band2（蓝光） 0.490 10 Band3（绿光） 0.560 10 Band4（红光） 0.665 10 Band5（红边1） 0.705 20 Band6（红边1） 0.740 20 Band7（红边1） 0.783 20 Band8（近红外） 0.842 10 Band8A（红边4） 0.865 20 Band11（短波红外1） 1.614 20 Band12（短波红外2） 2.202 20 
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表 2 主成分协方差特征向量矩阵及统计分析
Table 2. Principal component covariance eigenvector matrix and statistical analysis
项目 PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 Band2 −0.018 0.321 −0.287 −0.122 −0.582 −0.115 0.328 0.093 −0.154 −0.555 Band3 −0.041 0.316 −0.426 −0.081 −0.159 0.209 0.314 −0.283 0.015 0.680 Band4 −0.037 0.441 −0.360 −0.022 0.106 0.324 −0.670 0.021 0.263 −0.198 Band5 −0.111 0.350 −0.235 0.237 0.566 −0.234 0.195 0.453 −0.368 0.023 Band6 −0.390 −0.034 −0.132 0.359 0.263 −0.097 0.324 −0.420 0.504 −0.292 Band7 −0.469 −0.151 −0.117 0.313 −0.208 −0.155 −0.375 −0.341 −0.564 0.048 Band8 −0.529 −0.166 −0.112 −0.791 0.217 −0.066 0.013 0.028 −0.022 −0.043 Band8A −0.499 −0.152 0.046 0.253 −0.332 0.234 0.023 0.622 0.269 0.192 Band11 −0.245 0.401 0.588 −0.015 0.087 0.544 0.172 −0.155 −0.247 −0.120 Band12 −0.153 0.494 0.400 −0.073 −0.163 −0.629 −0.174 −0.025 0.255 0.222 特征值 2701053.3 463642.1 197268.0 41346.4 18464.4 6353.0 4798.2 3807.2 3143.7 2354.6 贡献率 0.785 0.135 0.057 0.012 0.005 0.002 0.001 0.001 0.001 0.001 累计贡献率 0.785 0.919 0.977 0.989 0.994 0.996 0.997 0.998 0.999 1.000
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表 3 主成分图像云模型参数
Table 3. Principal component image cloud model parameters
模型参数 PC1 PC2 PC3 $ {E}_{x} $ −3480 −1357 −276 $ {E}_{n} $ 783 206 95 $ {H}_{e} $ 187 44 20 CD 0.72 0.64 0.63
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