基于随机森林的水稻信息提取研究

Research on Extraction Method of Rice Information Based on Random Forest

以重庆市永川区朱沱镇Sentinel-2多光谱影像为例,构建随机森林分类模型,分别以单时相和多时相特征变量集为变量提取水稻空间分布,并对水稻对不同波谱特征集的响应程度及提取精度进行分析。分类结果显示,研究区水稻分布相对较为分散,且地块特征较为复杂,与区域典型地貌基本相适应;处于分蘖期的水稻稻田比处于灌浆期的稻田更有区分特征,利用多时相数据能够有效提高提取精度;通过传统的最大似然法、光谱角分类器提取地物精度有限,而基于机器智能分类的随机森林模型提取方法提取结果总体精度90%以上,Kappa系数达到0.80以上,可为西南山地地区作物信息提取提供参考。

Taking Sentinel-2 multispectral image of Zhutuo, Yongchuan district of Chongqing as an example,a random forest classification model was constructed, and rice spatial distribution was extracted using single-time and multi-time feature variable sets as variables,and the response degree and extraction accuracy of rice to different spectral feature sets were analyzed. The results show that the rice distribution in the study area is relatively scattered and complex,which is basically compatible with the typical landform of the region. Rice at tillering stage have more distinguishing characteristics than that at filling stage,and the extraction accuracy can be effectively improved by using multi-temporal data. Traditional MLC and SAM classifiers have limited precision in extracting ground objects,while remote sensing model based on machine intelligence classification has an overall precision of more than 90% and kappa coefficient of more than 0.80,which provides a reference for crop information extraction in southwest mountainous areas.