基于随机森林法的农作物遥感识别方法研究--以阿荣旗部分区域为例

Research on crop remote sensing recognition method based on a random forest method--Take some areas of Arun Banner as an example

【目的】为未来大尺度农作物遥感识别方法提供借鉴。【方法】以内蒙古阿荣旗部分区域为例,采用Sentinel2A影像主要数据源,利用随机森林法结合农作物光谱特征、纹理特征、参数特征,提取研究区的玉米、高粱、大豆、甜菜种植面积信息,并对分类结果进行精度验证和评价。【结果】4种农作物的分类总体精度达到80.02%,Kappa系数为0.7277,分类精度较好。【结论】利用随机森林分类器结合均值飘移算法能够明显改善影像"椒盐现象",并提高农作物的分类精度。

【Objective】It can provide a reference for future large-scale crop remote sensing recognition method.【Methods】We took the Sentinel2A images as the main source of Arun Banner in Inner Mongolia.Based on a random forest method and the full utilization of the spectral features,texture features,and parameter features to extract the area of plants including corn,sorghum,soybean,and sugar,the classification results were analzyed.【Results】The overall accuracy of the crop classification reached 80.02%,the Kappa coefficient achieved 0.7277,and the classification results are good.【Conclusion】Using random forest as a classifier combined with the way of mean shift it can ameliorate the phenomenon of"salt and pepper"and improve the accuracy of classification of crops.