华南地区典型种植园地遥感分类研究
发布时间：2019-05-23 23:27
【摘要】：华南地区种植园地广泛分布,类型混杂多样,导致园地分布信息难以正确获取,为农业管理造成了较大困难。本研究基于Landsat8 OLI数据,通过数据融合、特征优化,应用随机森林算法构建面向对象的种植园地分类规则集,对华南地区典型经济作物香蕉、柑橘、葡萄、蒲葵、海枣、番木瓜和火龙果等进行类别识别,同时对比贝叶斯分类法、K最邻近分类法、支持向量机法、决策树分类法的分类效果。结果表明:数据融合会在一定程度上影响分类结果精度;植株形态、光谱特征接近,种植期交错是影响华南地区典型园地分类精度的重要原因;以中分辨率影像为数据源,面向对象的随机森林算法应用于种植园地分类研究总体精度可达88.05%,Kappa系数0.87,可以有效区分华南地区典型种植园地类别;相比于其他算法,随机森林算法在分类精度、可靠性和稳定性上具有一定优势,可为园地作物生长监测和种植管理提供科学依据。
[Abstract]:The planting gardens in South China are widely distributed and the types are mixed and diverse, which makes it difficult to obtain the information of garden distribution correctly, which makes it difficult for agricultural management. Based on Landsat8 OLI data, through data fusion and feature optimization, an object-oriented classification rule set of planting gardens was constructed by using random forest algorithm. Banana, citrus, grape, sunflower and sea jujube, a typical cash crop in South China, were constructed. Papaya and dragon fruit were identified by category recognition, and the classification effects of Bayesian classification, K nearest neighbor classification, support vector machine method and decision tree classification were compared. The results showed that data fusion would affect the accuracy of classification results to a certain extent, and the plant morphology and spectral characteristics were close, and the interlaced planting period was an important reason for the classification accuracy of typical gardens in South China. Taking the medium-resolution image as the data source, the object-oriented stochastic forest algorithm can be applied to the classification of planting gardens with the overall accuracy of 88.05%, and the Kappa coefficient is 0.87, which can effectively distinguish the typical planting garden types in South China. Compared with other algorithms, stochastic forest algorithm has some advantages in classification accuracy, reliability and stability, and can provide scientific basis for crop growth monitoring and planting management in garden land.
【作者单位】： 西南大学地理科学学院三峡库区生态环境教育部重点实验室;中国科学院深圳先进技术研究院;
【基金】：深圳市科技计划项目(JCYJ20150831194835299) 国家重点研发计划子课题(2016YFC0500201-07)
【分类号】：S127
本文编号：2484332
[Abstract]:The planting gardens in South China are widely distributed and the types are mixed and diverse, which makes it difficult to obtain the information of garden distribution correctly, which makes it difficult for agricultural management. Based on Landsat8 OLI data, through data fusion and feature optimization, an object-oriented classification rule set of planting gardens was constructed by using random forest algorithm. Banana, citrus, grape, sunflower and sea jujube, a typical cash crop in South China, were constructed. Papaya and dragon fruit were identified by category recognition, and the classification effects of Bayesian classification, K nearest neighbor classification, support vector machine method and decision tree classification were compared. The results showed that data fusion would affect the accuracy of classification results to a certain extent, and the plant morphology and spectral characteristics were close, and the interlaced planting period was an important reason for the classification accuracy of typical gardens in South China. Taking the medium-resolution image as the data source, the object-oriented stochastic forest algorithm can be applied to the classification of planting gardens with the overall accuracy of 88.05%, and the Kappa coefficient is 0.87, which can effectively distinguish the typical planting garden types in South China. Compared with other algorithms, stochastic forest algorithm has some advantages in classification accuracy, reliability and stability, and can provide scientific basis for crop growth monitoring and planting management in garden land.
【作者单位】： 西南大学地理科学学院三峡库区生态环境教育部重点实验室;中国科学院深圳先进技术研究院;
【基金】：深圳市科技计划项目(JCYJ20150831194835299) 国家重点研发计划子课题(2016YFC0500201-07)
【分类号】：S127
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