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

    • In the field of remote sensing recognition, experts have proposed the JAM-R-CNN deep network model, which effectively improves the accuracy of terrace recognition and has practical value.
    • Vol. 28, Issue 12, Pages: 3136-3146(2024)   

      Published: 07 December 2024

    • DOI: 10.11834/jrs.20233126     

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  • Xie J Y,Lin A Q,Wu H,Wu Z W,Wu W B and Yu Q Y. 2024. JAM-R-CNN deep learning network model for remote sensing recognition of terraced fields. National Remote Sensing Bulletin, 28(12):3136-3146 DOI: 10.11834/jrs.20233126.
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相关作者

LIN Anqi 华中师范大学 城市与环境科学学院;地理过程分析与模拟湖北省重点实验室
WU Ziwei 华中师范大学 城市与环境科学学院;地理过程分析与模拟湖北省重点实验室
WU Wenbin 中国农业科学院 农业资源与农业区划研究所
YU Qiangyi 中国农业科学院 农业资源与农业区划研究所
SHAO Pan 三峡大学 湖北省水电工程智能视觉监测重点实验室;三峡大学 计算机与信息学院
GAO Ziang 三峡大学 湖北省水电工程智能视觉监测重点实验室;三峡大学 计算机与信息学院
LEI Xiangda 南京信息工程大学 遥感与测绘工程学院
GUAN Haiyan 南京信息工程大学 遥感与测绘工程学院

相关机构

Hubei Key Laboratory of Intelligent Vision Monitoring for Hydroelectric Engineering, China Three Gorges University
College of Computer and Information Technology, China Three Gores University
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University
School of Cyber Science and Technology, University of Science and Technology of China
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