面向梯田遥感识别的JAM-R-CNN深度网络模型  

JAM-R-CNN deep learning network model for remote sensing recognition of terraced fields

作　　者：谢君洋 林安琪 吴浩 吴紫薇 吴文斌[3] 余强毅[3] XIE Junyang;LIN Anqi;WU Hao;WU Ziwei;WU Wenbin;YU Qiangyi(College of Urban and Environmental Sciences,Central China Normal University,Wuhan 430079,China;Hubei Province Key Laboratory for Geographical Process Analysis and Simulation,Wuhan 430079,China;Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,China)

机构地区：[1]华中师范大学城市与环境科学学院,武汉430079 [2]地理过程分析与模拟湖北省重点实验室,武汉430079 [3]中国农业科学院农业资源与农业区划研究所,北京100081

出　　处：《遥感学报》2024年第12期3136-3146,共11页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家重点研发计划(编号:2022YFB3903500);湖北省自然科学基金创新群体项目(编号:2024AFA032);国家自然科学基金(编号:42071358,42201468);中央高校基本科研业务费资助(编号:CCNU22QN018);湖北省自然资源厅科研计划(编号:ZRZY2023KJ03)。

摘　　要：快速准确地掌握梯田的空间分布,不仅为水土保持提供重要的数据支撑,也提高了山区农业的监管水平。利用深度学习方法进行梯田识别,对于形状狭长的梯田容易因卷积运算而造成漏提现象,并且在山区地物边界不清晰的复杂背景下,易产生大面积粘连的识别结果,导致最终的梯田识别精度不高。为此,本研究提出了一种面向梯田遥感识别的JAM-R-CNN深度网络模型。该模型以Mask R-CNN为基础,融合跳跃网络来维持高分辨率遥感影像的高语义信息,引入卷积注意力机制模块来加强梯田的特征表达能力,修改模型的锚框大小以适应梯田狭长的特性。并以重庆市南川区的盐井梯田研究区域,基于国产高分二号(GF-2)卫星影像数据进行本研究模型验证实验。结果表明:JAM-R-CNN网络模型的梯田识别结果精确率为90.81%,召回率为84.28%,F1为88.98%,IoU为83.15%。相较于经典的Mask R-CNN模型,JAM-R-CNN网络模型的梯田识别精度较高,4个评价指标依次提升了1.96%、5.26%、3.29%和5.19%。此外,消融实验结果验证了模型改进的3个模块均对梯田识别有明显的促进作用。综上,本研究提出的JAM-R-CNN深度网络模型能够有效减少梯田识别结果的粘连现象,明显提高了狭长型梯田的提取率,实现了梯田遥感识别整体精度的显著提升,具有较好的应用价值。Efficiently and accurately determining the spatial distribution of terraced fields provides important data support for soil and water conservation and improves the regulatory level of agriculture in mountainous areas.When deep learning methods are used for terrace recognition,narrow and elongated terraces are prone to be missed because of convolution operations,and in complex backgrounds with unclear terrain boundaries in mountainous areas,large areas of adhesive recognition results are easily generated,leading to low accuracy in the final terrace recognition.Prior to the achievement of accurate recognition of terrace information,the urgent technical problems to be solved are how to effectively maintain the high semantic information of high-resolution remote sensing images in the convolution operation process on the basis of the characteristics of terraces and how to reduce the omission of narrow and long terraces and the adhesion of recognition results.To address these problems,this study proposes the JAM-R-CNN deep learning network terrace recognition method that adopts remote sensing images with very high resolution.This network is based on the Mask Region-based Convolutional Neural Network(Mask R-CNN)model.It integrates the jumping network to maintain the high semantic information of high-resolution remote sensing images,employs the convolutional block attention module to enhance the feature expression ability of terraces,and modifies the anchor size to adapt to the narrow and long characteristics of terraces and improve terrace recognition accuracy.A part of the salt well terraces in Nanchuan District,Chongqing,China,is selected as the study area to test the proposed method,and four models in domestic GF-2 satellite image data are used for experiments.Results show that the terrace parcel map derived from the JAM-R-CNN model has a precision of 90.81%,recall of 84.28%,F1 score of 88.98%,and Intersection over Union(IoU)value of 83.15%.Compared with Mask R-CNN,JAM-R-CNN’s precision,recall,F1 score,and IoU valu

关 键 词：遥感 梯田识别 高分辨率遥感影像 深度学习 跳跃网络 JAM-R-CNN 

分 类 号：S127[农业科学—农业基础科学] P2[天文地球—测绘科学与技术]