农业机械学报2026，Vol.57Issue(17)：31-41,11.DOI:10.6041/j.issn.1000-1298.2026.17.003
基于高分二号影像和改进DeepLabV3+的冬小麦种植区提取模型研究
Extraction Model Study of Winter Wheat Planting Areas Based on GF-2 Imagery and Improved DeepLabV3+Model
摘要
Abstract
Accurate acquisition of winter wheat spatial distribution information is fundamental to growth monitoring and yield prediction.To address the problems of severe spectral confusion,insufficient utilization of frequency-domain information,and loss of field edge details in remote sensing extraction of winter wheat under complex planting environments,an improved DeepLabV3+model named RSMANet was proposed for extracting winter wheat planting areas by using Chinese GF-2 imagery.Firstly,SC-ResNet18 was designed to replace the original backbone network by introducing the spatial and channel reconstruction convolution(SCConv),which improved the model's ability to extract spatial details and semantic features.Secondly,a multi-scale and multi-frequency attention(SCC-MFMSA)module was constructed in the encoding stage,consisting of three components:scale decomposition,multi-frequency channel attention(MFCA),and multi-scale spatial attention(MSSA).By synergistically extracting frequency-domain and spatial-domain features of the imagery through a dual-attention mechanism,this module effectively distinguished winter wheat canopy texture from background high-frequency noise,and improved the model's multi-scale perception ability.Finally,a multi-scale attention fusion(MAF)module was introduced in the feature fusion stage to achieve dynamic fusion of local detailed features and global semantic information.By enhancing feature representation and semantic correlation at channel,spatial,and multi-scale levels,the problems of blurred plot boundaries and severe spectral confusion were improved.Experimental results demonstrated that the RSMANet model effectively improved the extraction accuracy of winter wheat planting areas;the IoU and F1 score reached 89.09％and 94.52％,respectively,which were 3.19 and 1.39 percentage points higher than those of the original DeepLabV3+model,and 1.08～3.81 and 0.23～1.74 percentage points higher than those of other mainstream models(such as CMTFNet,SACANet,and TransUNet).The research results can provide a reference for winter wheat planting area extraction based on high-resolution imagery.关键词
冬小麦/种植区提取/高分二号影像/DeepLabV3+/多尺度多频注意力模块Key words
winter wheat/planting area extraction/GF-2 imagery/DeepLabV3+/multi-scale and multi-frequency attention分类
信息技术与安全科学引用本文复制引用
宋荣杰,汪强,史汶龙,屠晓雨,张宏鸣..基于高分二号影像和改进DeepLabV3+的冬小麦种植区提取模型研究[J].农业机械学报,2026,57(17):31-41,11.基金项目
农业农村部科技项目和国家重点研发计划项目(2020YFD1100601) （2020YFD1100601）
