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Convolutional Neural Network-Based Method for Agriculture Plot Segmentation in Remote Sensing Images

文献类型：期刊论文

作者Qi, Liang2; Zuo, Danfeng2; Wang, Yirong2; Tao, Ye2; Tang, Runkang2; Shi, Jiayu2; Gong, Jiajun2; Li, Bangyu1,2
刊名REMOTE SENSING
出版日期2024
卷号16期号:2页码:22
关键词plot segmentation convolutional neural networks remote sensing images TransUNet
DOI10.3390/rs16020346
通讯作者Li, Bangyu(bangyu.li@ia.ac.cn)
英文摘要Accurate delineation of individual agricultural plots, the foundational units for agriculture-based activities, is crucial for effective government oversight of agricultural productivity and land utilization. To improve the accuracy of plot segmentation in high-resolution remote sensing images, the paper collects GF-2 satellite remote sensing images, uses ArcGIS10.3.1 software to establish datasets, and builds UNet, SegNet, DeeplabV3+, and TransUNet neural network frameworks, respectively, for experimental analysis. Then, the TransUNet network with the best segmentation effects is optimized in both the residual module and the skip connection to further improve its performance for plot segmentation in high-resolution remote sensing images. This article introduces Deformable ConvNets in the residual module to improve the original ResNet50 feature extraction network and combines the convolutional block attention module (CBAM) at the skip connection to calculate and improve the skip connection steps. Experimental results indicate that the optimized remote sensing plot segmentation algorithm based on the TransUNet network achieves an Accuracy of 86.02%, a Recall of 83.32%, an F1-score of 84.67%, and an Intersection over Union (IOU) of 86.90%. Compared to the original TransUNet network for remote sensing land parcel segmentation, whose F1-S is 81.94% and whose IoU is 69.41%, the optimized TransUNet network has significantly improved the performance of remote sensing land parcel segmentation, which verifies the effectiveness and reliability of the plot segmentation algorithm.
资助项目Jiangsu Provincial Department of Science and Technology
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001151565800001
出版者MDPI
资助机构Jiangsu Provincial Department of Science and Technology
源URL[http://ir.ia.ac.cn/handle/173211/55397]  
专题自动化研究所_空天信息研究中心
自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者Li, Bangyu
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Jiangsu Univ Sci & Technol, Sch Automat, Zhenjiang 212003, Peoples R China
推荐引用方式
GB/T 7714		 Qi, Liang,Zuo, Danfeng,Wang, Yirong,et al. Convolutional Neural Network-Based Method for Agriculture Plot Segmentation in Remote Sensing Images[J]. REMOTE SENSING,2024,16(2):22.
APA Qi, Liang.,Zuo, Danfeng.,Wang, Yirong.,Tao, Ye.,Tang, Runkang.,...&Li, Bangyu.(2024).Convolutional Neural Network-Based Method for Agriculture Plot Segmentation in Remote Sensing Images.REMOTE SENSING,16(2),22.
MLA Qi, Liang,et al."Convolutional Neural Network-Based Method for Agriculture Plot Segmentation in Remote Sensing Images".REMOTE SENSING 16.2(2024):22.

入库方式： OAI收割

来源：自动化研究所

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