利用RefineNet模型提取冬小麦种植信息的方法

冬小麦是我国主要的粮食作物，获取精细的冬小麦种植信息对于指导农业生产具有重要的意义。通过对RefineNet模型进行扩展，形成了适宜提取冬小麦种植信息的Ex-RefineNet（Extend-RefineNet）模型，Ex-RefineNet模型由两个子模型组成，Ex-RefineNet-Edge子模型用于提取冬小麦种植区域的边缘像素，Ex-RefineNet-Inner子模型用于提取冬小麦种植区域的内部像素，使用贝叶斯模型对两个子模型的提取结果进行合并处理，形成最终提取结果。利用山东省济南市和泰安市的16幅高分2号遥感影像进行实验，将每幅影像的2/3作为训练数据，其他数据作为测试数据，选择平均精度、查全率和Kappa系数作为对比指标，Ex-RefineNet模型的结果分别为0.93、0.92、0.91，而RefineNet模型的结果分别为0.86、0.84、0.83，说明本文给出的方法在提取冬小麦种植信息方面具有较明显的优势。

Extraction Method for Winter Wheat Planting Area based on RefineNet

Winter wheat is the main food crop in Shandong area. It is of great significance to obtain accurate information of winter wheat planting structure for the study of food security. By expanding the RefinNet model, an Ex-RefineNet(Extend-RefineNet) suitable for extracting the information of winter wheat planting structure was formed. Ex-RefineNet consists of two submodels, the Ex-RefineNet-Edge submodel used to extract the edge pixels of the winter wheat growing area, Ex-RefineNet-Innner submodel is used to extract the inner pixels of winter wheat growing area. Finally, using Bayesian model the extraction results of the sub-model are merged to form the final extraction results. A total of 16 GF-2 images were used for comparative experiments in Jinan City and Tai'an City, Shandong Province, and 2/3 of each image was used as training data and other data were used as test data. In terms of average accuracy, total search rate, and Kapp-coefficient, results of the Ex-RefineNet model were 0.93, 0.92, and 0.91, respectively, while results of the RefineNet model were 0.86, 0.84, and 0.83, respectively. The extraction effect of the Ex-RefineNet model is significantly higher than that of the RefineNet model. Results showed that the Ex-RefineNet is advantageous to extract the structure of winter wheat.