利用边缘辅助分割网络提取稻虾共作养殖田

为确保稻虾共作的安全生产、社会供应以及政府政策的有效制定,须准确获取稻虾共作种植面积、空间分布及变化信息。现有稻虾养殖田提取方法以中分辨率时序影像为数据源,提取结果存在边界粗糙且噪声较多等问题。为获取精度高、边界规整的稻虾养殖田提取结果,该研究以高分辨率影像为数据源,提出一种基于边缘辅助任务的深度学习语义分割模型——edge assisted segmentation network（EASNet）。该模型首先将稻虾养殖田特有的边缘“虾沟”作为一种辅助信息在设计的边缘辅助模块单独分割,然后将该模块的输出与主任务分割模块输出进行融合,使主任务既增强了稻虾养殖田边界结构信息,又能学习到稻虾养殖田特有的空间及语义信息。试验结果表明,在边缘辅助模块的增强下,稻虾养殖田分割结果更完整,边界更清晰,其语义精度的交并比和边界精度的F1分数分别提升了1.5%、5.8%。整体语义精度的召回率、交并比、F1分数分别达到0.970、0.964、0.930,边界精度的召回率、F1分数达到0.864、0.859,松弛边界精度的召回率、F1分数达到0.876、0.913。将训练好的EASNet模型应用到盱眙县全域,得到2020年盱眙县稻虾养殖田空间分布图,在与传统的水体季相差异法、随机森林方法提取的稻虾养殖田结果的对比中,该方法取得了总体精确度为96.71%及Kappa系数为0.934的最优结果,为基于深度学习的稻虾养殖田提取方面的应用提供参考。

Extraction of rice and shrimp co-cultivation farming fields using edge-assisted segmentation network

Rice shrimp co-cultivation, as a circular agriculture model that integrates ecological, economic, and social benefits, has been widely applied in China. Accurately obtaining information on the planting area, spatial distribution, and changes of rice shrimp co-cultivation is of great significance for ensuring safe production, social supply of rice and shrimp, and formulating corresponding management and planning policies by government departments. Most of the existing methods for extracting rice shrimp farming fields used medium-resolution temporal images as data sources to analyze the characteristics of water or vegetation changes within a year to construct models. The accuracy of the extraction depended on the results of water or vegetation analysis, the boundaries were unclear and there was a lot of noise. To solve the problems of complex temporal analysis, low accuracy of extraction results, and incomplete boundaries in existing extraction methods, this paper used high-resolution images of a single temporal phase as the data source, without analyzing the temporal features of rice shrimp farming fields. Only the spatial features of rice shrimp farming fields on high-resolution images were used for high-precision extraction. We proposed a deep learning semantic segmentation model named EASNet (edge assisted segmentation network), which was mainly composed of three modules: feature extraction module (FE Module), edge assist module (EA Module), and information fusion Module (IF Module). The FE Module was used to extract multi-level and fused contextual semantic information features of the target object. The EA module used the unique edge "shrimp groove" of rice and shrimp farming fields as an auxiliary information for separate segmentation in the designed EA Module. The IF Module integrated the outputs of the FE Module and the EA Module, enabling the main task to not only enhanced the boundary structure information of rice shrimp farming fields but also learned the unique spatial and semantic information of rice shrimp farming fields. The experimental results showed that with the enhancement of the EA Module, the segmentation results of rice shrimp farming fields were more complete and the boundaries were clearer. The IoU (Intersection over Union) of semantic accuracy and the F1-Score of boundary accuracy were improved by 1.5% and 5.8%, respectively. The recall, IoU, and F1-Score of overall semantic accuracy reached 0.970, 0.964, and 0.930, respectively. The Recall and F1-Score of boundary accuracy reached 0.864 and 0.859, while the Recall and F1-Score of relaxed boundary accuracy reached 0.876 and 0.913. The trained EASNet model was applied to the whole area of Xuyi County, and the spatial distribution map of rice shrimp farming fields in Xuyi County in 2020 was obtained. In comparison with the results of rice shrimp breeding fields extracted by the traditional Water Seasonal Difference method and the Random Forest method, our method obtained the optimal results with an OA(overall accuracy) of 96.71% and a Kappa coefficient of 0.934. The method used in this paper has higher accuracy, lower missing rate, more regular boundaries, and fewer instances of similar integration in extracting results. It can provide a basis for natural resource surveys, and government departments to formulate corresponding rice shrimp breeding management and planning policies.