先验知识融合语义特征的冬小麦田块精细提取方法  

Fine extraction of winter wheat farmland parcel using priori knowledge and semantic features

作　　者：程傲 任子由 张承明[1,2] 李峰 吴门新[4] 李红英 段金馈[3] 刘一笑[7] CHENG Ao;REN Ziyou;ZHANG Chengming;LI Feng;WU Menxin;LI Hongying;DUAN Jinkui;LIU Yixiao(School of Information Science and Engineering,Shandong Agricultural University,Tai'an 271018,China;Shandong Digital Agriculture Engineering Technology Research Center,Tai'an 271018,China;Shandong Climate Center,Jinan 250031,China;National Meteorological Center,Beijing 100081,China;Ningxia Institute of Meteorological Sciences,Yinchuan 750001,China;Key Laboratory for Meteorological Disaster Monitoring,Early Warning and Risk Management of Characteristic Agriculture in Arid Regions,China Meteorological Administration/Ningxia Key Lab of Meteorological Disaster Prevention and Reduction,Yinchuan 750001,China;School of Data and Computer Science,Shandong Women’s University,Jinan 250300,China)

机构地区：[1]山东农业大学信息科学与工程学院,泰安271018 [2]山东省数字农业工程技术研究中心,泰安271018 [3]山东省气候中心,济南250031 [4]国家气象中心,北京100081 [5]宁夏回族自治区气象科学研究所,银川750001 [6]中国气象局旱区特色农业气象灾害监测预警与风险管理重点实验室/宁夏回族自治区防灾减灾重点实验室,银川750001 [7]山东女子学院数据科学与计算机学院,济南250300

出　　处：《农业工程学报》2025年第4期164-174,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：山东省自然科学基金项目(ZR2021MD097);山东省大学生创新创业训练项目(S202310434226);宁夏自然科学基金项目(2024AAC03419);风云卫星应用先行计划项目(FY-APP-ZX-202301)。

摘　　要：精细的田块数据是现代农业的重要基础资料,该研究针对从高分辨率遥感影像中提取田块精细数据的需求,建立了一种先验知识融合语义特征的冬小麦田块精细提取方法(prior knowledge and semantic features integration-based farmland parcel extraction methodology,PKFFPE),PKFFPE以遥感图像和相应的边缘图像作为输入,采用编码器-解码器结构进行特征提取,利用多尺度注意力模块捕获不同尺度的关键特征,使用SoftMax对图像进行初步分割;通过深入分析同一田块内颜色、纹理等特征的分布规律获取先验知识,利用先验知识建立后处理方法,对初分割结果进行优化,生成田块精细数据。选择河北省邯郸市馆陶县和山东省泰安市宁阳县作为试验区,用于验证PKFFPE方法在平原地区和丘陵地区的适用性;选择UNet、ErfNet、SegNet、EIGNet,以及面向对象分类的方法作为初分割的对比方法,选择条件随机场和形态学处理作为的后处理的对比方法开展对比试验。试验结果表明,PKFFPE方法在馆陶县、宁阳县结果的准确率(96.1%、93.2%)、精确率(90.6%、87.6%)、召回率(93.2%、90.6%)、和F1分数(91.9%,89.0%)均优于对比方法,证明了PKFFPE方法在从高分辨遥感影像中提取田块精细数据方面具有突出的优势,能够应用于科研和生产实践。Farmland parcel data can be accurately and rapidly acquired using remote sensing in modern agriculture.In this study,a prior knowledge and semantic features of integration-based farmland parcel extraction(PKFFPE)was proposed to accurately extract the winter wheat data from the high-resolution remote sensing images.The high-resolution remote sensing images were taken as the data source.Three modules were divided into the PKFFPE:an initial segmentation,a post-processing and an extraction module.Among them,the initial segmentation module was used to construct and then train a convolutional neural network model for the pixel-by-pixel classification.The post-processing module was to optimize the initial segmentation.The extraction module was to accurately extract the farmland parcel data from the inputted remote sensing images.The initial segmentation module of PKFFPE consisted of three parts:input,feature extractor and classifier.The input included the remote sensing and edge image blocks.It was also necessary to add the pixel-by-pixel labels into the input in the training stage;The feature extractor was employed an encoder-decoder structure.Two units of feature extraction were utilized to extract the semantic and edge features.The semantic feature extraction employed a multi-scale attention mechanism,including an improved channel and spatial attention mechanism.A multi-scale feature extraction was used to capture the features at different scales.The decoder shared the six levels of decoding units,each of which contained a number of convolutional layers.The columns and rows of the feature map were recovered,according to the inputted remote sensing image block each time.A SoftMax model was used as the classifier to classify each pixel,according to the feature map output from the decoder.Each pixel was labeled as either a winter wheat or a non-winter wheat.According to the prior knowledge derived from planting management,the pixels within the same farmland parcel generally exhibited the high consistency in the basic fe

关 键 词：遥感 分类 冬小麦 卷积神经网络 先验知识 田块 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]