| 冬小麦非监督分类结果的类别选择研究 |
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| 引用本文： | 王利民,刘佳,杨福刚,杨玲波,姚保民.冬小麦非监督分类结果的类别选择研究[J].中国农学通报,2019,35(6):134-142. |
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| 作者姓名： | 王利民 刘佳 杨福刚 杨玲波 姚保民 |
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| 作者单位： | 中国农业科学院农业资源与农业区划研究所 |
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| 基金项目： | 国家重点研发计划课题“作物生长与生产力卫星遥感监测预测”(2016YFD0300603)。 |
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| 摘 要： | 为了实现基于遥感影像作物的自动分类，发挥遥感技术宏观、迅速的大范围监测特点，本文在遥感影像非监督分类的基础上，提出了一种基于ISODATA非监督分类结果的自动分类方法。该方法分为ISODATA非监督分类过程和自动分类过程，自动分类过程又可分为冬小麦样本点占比排序和类冬小麦类别确定两个方面。当非监督分类类别设置为40或50类、每类样本数量为4或5类时，冬小麦遥感分类精度较高且分类精度稳定。在200个样本点组合（40个分类类别，每个类别中5个样本点）中，基于ISODATA非监督自动分类结果的总体精度相较于最大似然分类方法提高了2.5个百分点，KAPPA系数提高了19.4%。在500个样本点组合（100个分类类别，每个类别中5个样本点）下，基于ISODATA非监督自动分类结果总体精度和KAPPA系数与最大似然分类方法相近。基于ISODATA非监督分类结果的自动分类方法可以在样本量较少时保持较高的分类精度，人机交互少，分类效率高，适用于业务化应用。
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| 关 键 词： | 新常态 新常态 城乡 收入分配结构 优化 |
| 收稿时间： | 2018/9/4 0:00:00 |
| 修稿时间： | 2019/1/21 0:00:00 |
Category Selection of Winter Wheat Unsupervised Classification Result |
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| Abstract: | To achieve the crop automatic classification based on remote sensing images and take advantage of the characteristics of macro, rapid and large scale monitoring, based on the remote sensing image unsupervised classification, an automatic classification method on the basis of ISODATA unsupervised classification was provided. The method had ISODATA unsupervised classification process and automatic classification process. The automatic classification process included the proportion ranking of winter wheat sample and winter wheat identification. When the classification categories of unsupervised classification were set to be 40 or 50 respectively, and each category had 4 or 5 samples, the winter wheat classification accuracy was relatively high and stable. Among the 200 sample point combinations (40 classified categories, each category with 5 sample points), the overall accuracy of winter wheat classification based on automatic ISODATA unsupervised classification method was 2.5 percent higher than that of the maximum likelihood classification method and its KAPPA coefficient rose by 19.4%. Among 500 sample point combinations (100 classified categories, each
category with 5 sample points), the winter wheat overall accuracy and its KAPPA coefficient based on automatic ISODATA unsupervised classification method were close to that of the maximum likelihood classification method. The automatic ISODATA unsupervised classification method has relatively higher classification accuracies with fewer samples, and it is featured with less human- machine interaction and higher classification efficiency. Therefore, this method is suitable for operational applications. |
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| Keywords: | ISODATA GF-1 WFV winter wheat area remote sensing recognition |
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